According to a research study conducted by Zhenzhen Zhuang, Jiandong Chen, Hongfeng Xu, Yuwen Jiang, and Jialiang Lin from Guangzhou Institute of Science and Technology and Guizhou Normal University, large language models (LLMs) are transforming academic peer review through the introduction of Automated Scholarly Paper Review (ASPR). Their survey, titled Large Language Models for Automated Scholarly Paper Review: A Survey, provides a comprehensive overview of the coexistence phase between ASPR and traditional peer review, underscoring the transformative potential of LLMs in academic publishing.

The researchers examined how LLMs, such as GPT-4, are integrated into peer review processes, addressing key challenges such as technological bottlenecks and domain-specific knowledge gaps. They explored innovations like multimodal capabilities, iterative review simulations, new tools like MAMORX, and datasets such as ReviewMT that enhance ASPR’s effectiveness. The study also investigated the reactions of academia and publishers to ASPR and outlined the ethical concerns associated with these technologies, such as biases and data confidentiality risks.

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1. The emergence of Automated Scholarly Paper Review (ASPR)

Large Language Models (LLMs) have ushered in a new era for academic peer review through the concept of Automated Scholarly Paper Review (ASPR). This approach harnesses the computational power of LLMs to transform traditional, human-led peer reviews into efficient, unbiased, and scalable processes. With ASPR, academia is witnessing a paradigm shift toward technology-driven precision.

1.1 What is ASPR?

Automated Scholarly Paper Review (ASPR) is a system that integrates LLMs to manage and optimize peer review tasks. By automating essential activities like summarizing manuscripts, identifying errors, and generating detailed feedback, ASPR ensures rigor that matches, and often surpasses, traditional methods. It doesn’t merely enhance human efforts; it redefines the framework of academic evaluations.

ASPR relies on advanced models like GPT-4 to deliver consistent, high-quality evaluations. These models are trained to process extensive text, assess complex methodologies, and provide unbiased feedback, making ASPR a game-changing innovation for scholarly publishing.

1.2 Why academia needs ASPR

The peer review process is often criticized for being slow, inconsistent, and influenced by subjective biases. These inefficiencies delay the publication timeline and affect the credibility of academic output. ASPR directly addresses these flaws with its ability to rapidly analyze manuscripts and generate actionable insights.

Through LLMs, ASPR delivers precise and reliable reviews at an unprecedented speed. It identifies ethical concerns, checks for methodological accuracy, and ensures adherence to academic standards. For a sector under constant pressure to publish rigorously and swiftly, ASPR provides the necessary technological boost to uphold academic integrity while meeting growing demands.

2. Key technologies driving ASPR

ASPR’s transformative potential stems from the integration of cutting-edge LLM capabilities. These technologies tackle longstanding challenges in peer review, offering new ways to process complex academic content and simulate human interactions. These technologies’ evolution lays the groundwork for a more efficient and reliable peer review ecosystem.

2.1 Long text and multimodal processing

Writing long-form scholarly content has always been challenging, but LLMs have significantly advanced the field. Models like GPT-4 can now process extensive texts—up to 64,000 tokens—enabling detailed analysis of entire manuscripts in one pass. This ensures that every aspect of a paper, from introduction to references, is thoroughly reviewed.

Moreover, LLMs have become multimodal, meaning they can analyze text, figures, tables, and multimedia content. This capability ensures that reviews are comprehensive and account for all critical elements of a scholarly manuscript. It’s no longer just about text; the entire context of a paper is considered.

2.2 Multi-round review simulations

Peer review is iterative, often requiring multiple rounds of feedback and revisions. Traditional methods struggle with inefficiencies in this process, but LLMs excel in simulating multi-round interactions. By incorporating the back-and-forth dynamics between authors, reviewers, and editors, these models replicate the nuances of human-led reviews.

In practice, this means ASPR systems can suggest improvements, evaluate revisions, and offer further feedback in a structured and dynamic manner. This iterative capability ensures that manuscripts receive detailed and actionable critiques, aligning ASPR reviews closely with traditional academic expectations.

2.3 Emerging tools and datasets

ASPR’s rapid development is supported by an ecosystem of tools and datasets tailored for automated peer review. Platforms like MAMORX and Reviewer2 optimize the generation and evaluation of review comments. These tools work in tandem with datasets such as ReviewMT, which fine-tune models for specific academic domains and tasks.

These resources are more than just supporting structures; they are the foundation for ASPR’s scalability and adaptability. By enabling precise, domain-specific evaluations, these tools and datasets are driving ASPR closer to becoming the standard in scholarly publishing.

3. Challenges and ethical considerations

Adopting LLMs for Automated Scholarly Paper Review (ASPR) comes with its own challenges and ethical dilemmas. While these models showcase remarkable potential, their current limitations, risks to data confidentiality, and inherent biases demand scrutiny and robust solutions.

3.1 Limitations of current LLMs

Large Language Models are powerful, but they are not infallible. Inaccuracies and biases often emerge in their generated reviews, raising concerns about their reliability in critical academic evaluations. These issues stem from the models’ reliance on training data, which may not always reflect the nuances of specialized fields.

LLMs also struggle with domain-specific expertise. While they can process and generate general feedback efficiently, they lack the profound understanding required to evaluate cutting-edge or niche research topics. This gap limits their effectiveness in providing detailed, meaningful critiques.

3.2 Privacy and confidentiality concerns

Using cloud-based LLMs to review manuscripts introduces significant data security and confidentiality risks. Academic peer reviews require strict privacy protocols, and uploading unpublished work to third-party servers can lead to unintended data exposure.

To mitigate this, there are growing calls for deploying privately hosted LLMs. Such models would ensure that sensitive information remains within secure, institution-controlled environments, aligning with the confidentiality requirements of academic publishing.

3.3 Addressing bias in review comments

Bias in LLM-generated reviews is a critical challenge. Training data often carries biases related to geography, gender, or academic prestige, which can inadvertently influence the model’s evaluations. This affects the fairness of reviews and undermines trust in ASPR systems.

Mitigating bias requires targeted strategies, such as incorporating diverse and representative datasets during training and implementing bias-detection mechanisms within the review pipeline. By addressing these biases, ASPR can ensure that evaluations are equitable and impartial.

4. The future of ASPR

As LLMs evolve, so too does their role in reshaping academic peer review. ASPR is not just a technological upgrade; it is a glimpse into the future of scholarly evaluation. However, realizing this vision demands overcoming technical and ethical hurdles while aligning with academic norms.

4.1 Towards fully automated peer review

LLMs have enormous potential to standardize and streamline academic evaluations. By automating labor-intensive tasks, ASPR can establish a new benchmark for speed, accuracy, and consistency in peer reviews. This automation is particularly valuable as publication volumes grow exponentially.

Challenges remain, particularly in ensuring that ASPR systems can meet the rigorous demands of diverse academic disciplines. Addressing issues like domain expertise, adaptability, and the ability to evaluate novel research will be critical to achieving full-scale implementation.

4.2 Integration into academic norms

Adopting ASPR within traditional academic frameworks requires a careful balance. Publishers and academia must work collaboratively to establish guidelines that ensure transparency, fairness, and accountability in LLM-assisted reviews. Resistance to automation stems from fears of diminished human oversight. However, these concerns can be alleviated through clear policies and ethical safeguards.

Aligning LLMs with the core values of academic research\u2014rigor, integrity, and innovation\u2014is essential. As ASPR becomes a standard tool in scholarly publishing, its integration must reflect the collective goals of academia: fostering knowledge, advancing discovery, and maintaining the highest evaluation standards.

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OpenAI is set to revolutionize online education by integrating AI chatbots into courses. At a recent fireside chat hosted by Coeus Collective, Siya Raj Purohit of OpenAI’s education team explained the ongoing efforts to develop customized AI tools, called “GPTs,” for enhanced student engagement with course material. The initiative aims to provide students with tailored assistance throughout their learning process.

OpenAI enhances online education with AI chatbots

Purohit discussed how professors are currently utilizing OpenAI’s technology by uploading a complete semester’s worth of materials into AI models. These customized GPTs facilitate deeper interaction with specific subjects, potentially enhancing students’ research abilities. “This is a great way for students to interact with finite knowledge and improve their research skills,” Purohit emphasized, showcasing the aim of fostering more meaningful connections between students and educational content.

OpenAI’s commitment to the education sector is underscored by its recent hiring of Leah Belsky, former chief revenue officer of Coursera, as its first education general manager. Under her leadership, the company intends to propel its educational initiatives forward, aiming to broaden the reach of its AI-driven products in schools. Furthermore, the rollout of ChatGPT Edu—an adaptation of the chatbot specifically designed for university settings—exemplifies this aggressive strategy.

The potential market for AI in education appears lucrative. According to Allied Market Research, projections indicate that this sector could reach approximately $88.2 billion by 2033. OpenAI’s targeted efforts appear to align with these statistics as the company seeks to capitalize on burgeoning opportunities in educational technology.

Some evidence of success is visible in existing educational AI applications, such as Khan Academy’s Khanmigo, a chatbot that aids students with homework and test preparation. However, while these tools are intended to enhance learning, their effectiveness has met skepticism. For instance, Khanmigo has faced criticism for inaccuracies in basic tasks, such as mathematics, and sometimes fails to correct mistakes when prompted.

ChatGPT Pro is introduced: Is it worth $200?

Purohit contended that improvements in AI technology are underway. “All of our models keep getting better, and our goal is to help translate that into what works in learning and teaching,” she stated, signaling confidence in the evolving capabilities of AI in educational contexts.

Despite the enthusiasm from tech developers, educators express reservations. A Pew Research Center survey indicates that a quarter of public K-12 teachers believe AI tools do more harm than good in educational settings. Additionally, a recent study from the Rand Corporation and the Center on Reinventing Public Education revealed that only 18% of K-12 educators are incorporating AI into their classrooms.

Featured image credit: Solen Feyissa/Unsplash

The world of AI research is in constant flux, with breakthroughs emerging at a dizzying pace. But where are these advancements happening? While universities have traditionally been the hotbed of scientific discovery, a significant shift is underway. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry. 

In 2019, 65% of graduating North American PhDs in AI opted for industry roles, a significant jump from 44.4% in 2010. This trend highlights the growing influence of industry labs in shaping the future of AI.

To understand this evolving landscape, I spoke with Shakarim Soltanayev, a Research Scientist at Sony Interactive Entertainment and a former Research Engineer at Huawei. His insights shed light on the motivations, benefits, and challenges of conducting AI research within a large company and how this interplay with academia drives innovation.

Why Companies Embrace Academic Publishing

Tech giants like Google, Meta, Microsoft, and NVIDIA publish research at academic conferences for various reasons.

“First and foremost, publishing research at conferences can be a powerful marketing tool for companies,” Soltanayev said. “These publications serve as a form of indirect marketing, demonstrating the company’s technical prowess and commitment to advancing the field. This boosts their brand image within the research community and in the eyes of customers, partners, and investors. These publications help companies stand out from competitors and strengthen their overall market presence.”

The role that publishing plays in talent acquisition is vital.

“Top-tier conferences such as NeurIPS and CVPR are a prime venue for networking with leading researchers and engineers and recruiting promising students,” Soltanayev said. “By showcasing their work, research laboratories such as Google Deepmind and Meta AI can attract the brightest minds in the field, as top talent often wants to work on groundbreaking problems with access to high-quality resources and collaborators.”

A Two-Way Street: The Exchange of Value

The relationship between academia and industry is not one-sided; it’s a dynamic exchange of knowledge and resources that benefits both sides.

“A great example of academic research directly influencing industry is the development of the convolutional neural network (CNN) architecture,” Soltanayev said. “It was pioneered by Yann LeCun and his colleagues in the academic space, and it has had a major impact on tech products, particularly in computer vision. When AlexNet, a CNN-based model, won the ImageNet competition in 2012, it sparked widespread adoption in the industry. Nowadays, CNNs have a wide range of applications, including image recognition for facial identification and object detection, medical imaging for disease diagnosis, and autonomous vehicles for real-time object recognition.”

On the other hand, the industry has significantly contributed to academic research in several ways.

“One of the most notable contributions is the development of large-scale datasets and powerful computing frameworks,” Soltanayev said. “For example, companies have released massive datasets, such as those for image recognition, language models, and self-driving car simulations, that have become critical for academic research. These datasets provide the necessary scale for training advanced machine learning models, which would be difficult for most academic labs to collect independently. Industry also drives innovation in hardware and software, with the development of GPUs by NVIDIA and deep learning frameworks like TensorFlow by Google and PyTorch by Meta, now standard tools in academic and industrial research.”

Different Priorities, Different Cultures

As AI advances, academia and industry are taking different paths to prioritize and approach these developments.

“The main difference between academia and industry research is the focus,” Soltanayev said. “In academia, the priority is often on long-term, fundamental questions that push the boundaries of knowledge. Researchers have the freedom to explore ideas without the pressure of immediate application. In industry, research focuses more on solving real-world problems and creating products, so the timeline is usually shorter, and there’s more pressure to deliver practical results.”

The variations between the two environments significantly influence the cultural dynamics.

“Academia encourages deep exploration, independent thinking, and publishing findings to advance knowledge,” Soltanayev said. “Industry research, on the other hand, is more collaborative, with teams working together to quickly turn ideas into products or solutions. While academic research often provides the theoretical groundwork, industry research pushes innovation by applying these ideas in real-world situations.”

The Allure of Industry Labs

So, why are more researchers pursuing careers in industry labs rather than traditional academic institutions, and what are the advantages and disadvantages of each path?

“Many researchers are choosing to work at big companies due to the attractive compensation packages,” Soltanayev said. “Salaries in industry labs are typically much higher than those in academia, and they often come with additional benefits such as health insurance, retirement plans, and bonuses. In particular, stock options or equity can be a major draw, especially in tech companies where shares have the potential to grow significantly in value. These financial incentives can offer long-term security that’s harder to achieve in academia, where researchers may face grant-based funding cycles and lower salaries, especially in the early stages of their careers. The stability and benefits that big companies provide, combined with the opportunity to work on high-impact, well-funded projects, make industry labs an appealing choice for many.”

Industry research is often focused on achieving specific business goals and developing new products, which can limit researchers’ freedom to explore topics purely for the sake of knowledge.

“In contrast, academia offers the ability to pursue long-term, curiosity-driven projects, which can be deeply rewarding for those passionate about fundamental research,” Soltanayev said. “Academia also encourages the development of independent research programs and the ability to mentor and teach the next generation of scientists, which many researchers find fulfilling. That said, the “publish or perish” culture in academia can create pressure to produce papers frequently, which may sometimes limit the freedom to take big risks or explore novel ideas. Securing funding and tenure positions can also be highly competitive, adding to the stress of an academic career.”

The industry provides superior financial incentives, job security, and access to resources for tackling significant real-world challenges. On the other hand, academia offers greater intellectual autonomy and opportunities for self-directed research. Both paths have their own advantages, and the decision depends on the researcher’s personal motivations—whether they prioritize immediate impact and compensation or a deeper exploration of fundamental ideas.

The Future of Collaboration

Soltanayev envisions an even more intertwined future for academia and industry.

“I see the relationship between academia and industry in AI becoming even more collaborative,” Soltanayev said. “In the future, I expect to see more partnerships between universities and companies, where academic research provides the groundwork for industry to build upon, while companies provide the data, computing power, and funding necessary to drive large-scale experiments and applications. Companies will continue to play a major role in shaping AI’s future, particularly in applied research and development. With their vast amounts of data and access to powerful computing resources, they’re uniquely positioned to accelerate progress in machine learning, natural language processing, and computer vision.”

Organizations will maintain their influence on AI research by contributing to open-source projects, sharing data, and creating new tools and frameworks. This cooperative environment will play a critical role in expediting advancements in AI and ensuring its responsible progress. With the boundaries between academia and industry becoming increasingly indistinct, we can anticipate even more remarkable progress in AI, driven by the collaborative relationship between these two influential entities.

Learning how to become an AI engineer is an exciting and transformative journey that intertwines technical prowess, perpetual learning, and hands-on application. Aspiring AI engineers should concentrate on acquiring skills in machine learning, deep learning, and data analysis.

But why would anyone want to become an AI engineer? The answer lies in the immense potential and transformative power of artificial intelligence. AI is not just a fleeting trend; it is the cornerstone of future technology. From self-driving cars and virtual assistants to advanced healthcare diagnostics and personalized recommendations, AI is revolutionizing every aspect of our lives.

AI engineers are at the forefront of this technological revolution. By becoming an AI engineer, you have the opportunity to work on cutting-edge projects that redefine what is possible. The field is rich with innovation, constantly evolving, and full of opportunities for creative problem-solving.

Moreover, the demand for AI professionals is soaring. Industries across the globe are investing heavily in AI to enhance efficiency, drive innovation, and gain a competitive edge. As a result, AI engineers are highly sought after, often commanding impressive salaries and enjoying a wealth of career opportunities.

Additionally, the impact of AI on society is profound. AI has the potential to address some of the world’s most pressing challenges, from climate change to healthcare accessibility.

As an AI engineer, you have the chance to contribute to meaningful projects that make a difference in the world.

How to become an AI engineer

Learning how to become an AI engineer is a journey that combines technical expertise, continuous learning, and practical application. The path to becoming an AI engineer starts with building a strong foundation in computer science, mathematics, and programming. Aspiring AI engineers should focus on developing skills in machine learning, deep learning, and data analysis.

To become an AI engineer, you’ll need to gain proficiency in programming languages like Python, R, and Java, which are essential tools in AI development.

Education plays a crucial role in learning how to become an AI engineer. Many professionals in this field hold degrees in computer science or related disciplines. However, the journey to become an AI engineer doesn’t necessarily require a traditional degree. Online courses from IBM, Google, or Microsoft, boot camps, and self-study can also provide the knowledge needed to become an AI engineer. Regardless of the educational path chosen, hands-on experience is vital for those aspiring to become AI engineers.

Gaining practical experience is a key step in learning how to become an AI engineer. Participating in internships, contributing to open-source projects, and working on personal AI projects can help build a strong portfolio. Networking with other professionals and joining online communities can provide valuable insights for those looking to become AI engineers.

As you progress in your journey to become an AI engineer, staying up-to-date with the latest advancements in the field is crucial.

The ins and outs of AI engineering

As you delve deeper into how to become an AI engineer, you’ll discover the vast array of applications and possibilities that this technology offers. AI engineers work on developing intelligent systems that can perceive, learn, and interact with their environment. These systems are used across various industries, from healthcare and finance to automotive and entertainment.

One of the key aspects of becoming a successful AI engineer is staying current with the latest trends and techniques. AI is a rapidly evolving discipline, and those who want to become AI engineers must be committed to lifelong learning. Regularly reading research papers, following influential AI researchers, and participating in online courses can help you stay current as you work to become an AI engineer.

Understanding the ethical implications of AI is another crucial aspect of the journey to become an AI engineer. As AI systems become more prevalent in our daily lives, it’s essential to consider their potential impacts on society, privacy, and fairness. Those who become AI engineers must be mindful of these issues and strive to develop responsible and transparent AI solutions.

AI engineer salary insights

When considering how to become an AI engineer, it’s natural to be curious about potential earnings. AI engineers are in high demand across various industries, and their salaries reflect this demand. The compensation for those who become AI engineers can vary widely depending on factors such as experience, location, and the specific industry.

Entry-level professionals who have just learned how to become an AI engineer can expect competitive salaries, often starting around $90,000 – $120,000 per year in the United States. With a few years of experience, this can rise to $120,000 – $150,000.

Software engineering: An indispensable component of AI

Mid-level and senior AI engineers often command higher salaries, especially those working in tech hubs or for large technology companies. Experienced engineers in the U.S. can earn between $150,000 – $250,000 annually, with some top earners reaching $300,000 or more. In regions like Silicon Valley, salaries can be even higher.

In addition to base salaries, many who become AI engineers receive other forms of compensation, such as annual bonuses (ranging from 10% – 20% of base salary), stock options, and comprehensive benefits packages. Some companies also offer perks like flexible work arrangements and access to cutting-edge AI research and tools, which can be attractive to those who have recently learned how to become an AI engineer.

AI engineer jobs on the horizon

The job market for those who become AI engineers is robust and continues to grow as more industries recognize the value of AI technologies. AI engineer jobs can be found in a wide range of sectors, including technology, finance, healthcare, automotive, and retail. Some common job titles for those who have learned how to become an AI engineer include:

• Machine learning engineer
• Data scientist
• AI researcher
• Computer vision engineer

Large tech companies are known for hiring those who have mastered how to become an AI engineer to work on cutting-edge projects. However, opportunities are not limited to these tech giants. Startups, research institutions, and traditional companies across various industries are also actively seeking AI talent to drive innovation and improve their products and services.

Those who become AI engineers often have the flexibility to choose between different work environments. Some may prefer the fast-paced atmosphere of a startup, while others might opt for the stability and resources of a large corporation. Remote work opportunities are also becoming more common for those who have learned how to become an AI engineer, allowing professionals to work from anywhere in the world.

Charting your course in AI engineering

As you progress in your journey to become an AI engineer, you’ll encounter various specializations within the field. Some AI engineers focus on developing neural networks for image recognition, while others might specialize in natural language processing or reinforcement learning. Exploring these different areas can help you find your niche as you learn how to become an AI engineer.

Collaboration is a key aspect of AI engineering. Many AI projects require interdisciplinary teams, bringing together experts from various backgrounds. Learning to work effectively in diverse teams and communicate complex AI concepts to non-technical stakeholders is an essential skill for those who become AI engineers.

Continuous learning and adaptation are crucial for success in AI engineering. The field is constantly evolving, with new algorithms, tools, and techniques emerging regularly. Successful professionals who have learned how to become an AI engineer embrace this change and are always eager to apply new knowledge to their work.

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According to PISA ranking 2023, the children are not doing well at all. The most recent findings from the Programme for International Student Assessment (PISA), a leading global organization for ranking students, are causing concerns throughout Europe.

The Programme for International Student Assessment (PISA) stands as a beacon, illuminating the academic proficiency of 15-year-olds across the globe. Every three years, this comprehensive assessment, conducted by the Organisation for Economic Co-operation and Development (OECD), scrutinizes students’ abilities in reading, mathematics, and science, providing valuable insights into the effectiveness of educational systems worldwide.

The year 2023 marks the latest iteration of this crucial evaluation, and the results, released on December 5, 2023, paint a compelling picture of the global educational landscape. With 81 countries and economies participating, PISA 2022 delved into the intricacies of mathematics, complemented by an additional test of creative thinking.

What does PISA ranking 2023 show us?

PISA ranking 2023 unveiled some noteworthy trends that shape the current educational landscape. One striking observation is the widening gap between high-performing and low-performing countries. While students in top-ranking nations like China, Singapore, and Estonia continue to excel, those in less-advantaged regions struggle to keep pace. This disparity highlights the pressing need for equitable access to quality education worldwide.

Another notable trend is the increasing emphasis on creative thinking alongside traditional academic skills. PISA 2022’s focus on mathematics, coupled with the creative thinking assessment, underscores the growing recognition of non-cognitive skills in the 21st century. As innovation and adaptability become increasingly crucial for success, nurturing these abilities is paramount.

The top 10 countries according to PISA ranking 2023 are as follows:

1. Singapore – 560
2. Macau – 535
3. Taiwan – 533
4. Japan – 533
5. South Korea – 523
6. Hong Kong – 520
7. Estonia – 516
8. Canada – 506
9. Ireland – 504
10. Switzerland – 498

The data from PISA ranking 2023 provides valuable insights into the performance of different countries. One key finding is that Asian countries dominate the top rankings, with five regions – Singapore, Macau, Taiwan, Japan, and South Korea – holding the top five positions.

Another interesting trend is that lower-income countries, such as Vietnam, tend to perform better than wealthier nations, such as the United States, in these metrics. This could indicate that investments in education and socio-economic factors play a role in determining academic achievement.

Nordic countries, known for their progressive educational systems, show a mixed bag of results. While Estonia stands out for its exceptional performance, other Nordic countries like Norway, typically considered leaders in education, lag behind.

AI Chatbots and education cannot coexist under the same roof

The release of PISA ranking 2023 coincides with the advent of language models like ChatGPT, and artificial intelligence (AI) systems capable of generating human-quality text. While these models exhibit remarkable capabilities in producing coherent and grammatically correct prose, they lack the human element that underpins education.

ChatGPT, for instance, can effectively summarize factual information and craft engaging narratives. However, it falls short of comprehending the nuances of human experience, the ability to think critically, and the capacity to innovate. These qualities, essential for fostering well-rounded individuals, remain the domain of human education.

On the other hand, there is the use of artificial intelligence in exams and assignments, which is the most common complaint of educators. When we look at the world in general, many parents, with the exception of a few countries, argue that their children are not getting an adequate and proper education and that the current system of educate and test is not very suitable for today’s conditions.

But what makes this sub-optimal system work a little bit is that it forces children to think through exams and assignments. Young people and children are very active users of technology and keep up with current trends better than anyone else.

Therefore, it would not be wrong to think that they interpret artificial intelligence as a shortcut to success and they utilize it to have a way of skipping the system they need to think about by having artificial intelligence solve almost all the tests and do assignments for them. After all, ChatGPT has passed an MBA exam previously and the result is what PISA ranking 2023 shows us.

Featured image credit: Alexander Grey/Unsplash.

Students often grapple with the challenge of creating well-researched, articulate essays within tight deadlines. Lunchbreak AI is a great tool designed to transform this daunting task into a manageable, even enjoyable, experience. This innovative platform is rapidly becoming an indispensable asset for students worldwide, aiming to enhance their academic performance and writing skills.

What is Lunchbreak AI?

Lunchbreak AI is a state-of-the-art tool that utilizes advanced artificial intelligence to assist students in researching and writing essays. It’s a platform where technology meets academia, offering a unique solution to the often time-consuming process of essay writing.

The tool is engineered to generate plagiarism-free, research-backed essays, enabling students to produce high-quality content efficiently. With over 60,000 students already utilizing Lunchbreak AI, it’s evident that the platform is filling a vital need in the academic community.

The core philosophy of Lunchbreak AI is to empower students to achieve their best by providing them with a virtual study buddy. This tool is not just about easing the essay-writing process; it’s about enhancing the overall learning experience. Students can explore different perspectives, clarify ideas, and present well-rounded arguments in their essays, all facilitated by this AI-driven platform.

Humata AI is your helping hand in academic research

Features of Lunchbreak AI

1. AI-driven essay generation: At the heart of Lunchbreak AI is its ability to generate essays based on user inputs. Students can enter the topic, essay type, and length, and the tool will craft a comprehensive, well-structured essay. This feature is a game-changer for students who often struggle with where to start or how to structure their thoughts.
2. Plagiarism-free, research-backed content: One of the standout features of Lunchbreak AI is its commitment to academic integrity. The essays generated are plagiarism-free and backed by thorough research, ensuring that students can submit their work with confidence.
3. Topic and source generation: Lunchbreak AI goes beyond just writing essays; it assists in the initial stages of essay preparation. The platform can suggest relevant topics and generate real, cited sources and references, making the research phase much more efficient.
4. AI detection and humanization: A unique aspect of Lunchbreak AI is its AI detection and humanization feature. The tool ensures that the content it generates is undetectable as AI-written, while also training its model on over 40,000 student essays and research papers for authenticity.
5. User-friendly interface and workflow integration: The platform is designed to be intuitive and easy to use. It seamlessly integrates into a student’s workflow, acting as a virtual study assistant that is accessible at any time. This ease of use is critical in making the tool a part of the daily routine for students.
6. Flexible pricing and free trial: Understanding the financial constraints of students, Lunchbreak AI offers a free trial and a flexible pricing model. This approach ensures that the tool is accessible to a broad range of students, making advanced AI technology available for academic success.

How to use Lunchbreak AI?

Utilizing Lunchbreak AI for your essay writing needs is a remarkably simple and intuitive process:

• Begin your journey by visiting the official website of Lunchbreak AI. This is where the magic starts, and you’ll find all the resources you need to kickstart your essay.

• On the homepage, you’ll notice the “Start Writing” button. Clicking on this button will lead you to the core functionality of the platform where your essay writing journey begins.
• To personalize your experience, sign up using your Google account. This step ensures a seamless integration of your work and preferences on the platform.

• Now, it’s time to input your essay topic. For illustrative purposes, let’s use “Essentials of sentiment analysis” as our example topic. This is where you define the subject matter of your essay.
• Lunchbreak AI offers versatility in terms of essay types. Whether you need a narrative, expository, descriptive, or argumentative essay, you can select the type that best suits your assignment’s requirements.
• Next, choose the desired word count for your essay. This flexibility allows you to tailor the length of your essay to meet specific criteria or personal preferences.

• Once you’ve input all necessary information, click on “Generate Outline.” Lunchbreak AI will then present you with a well-structured outline based on your topic. This feature is incredibly helpful in giving you a clear roadmap for your essay.
• After reviewing the provided outline, select the topics that you find most relevant and compelling for your essay. This step is crucial as it shapes the direction and content of your final piece.

• Now, select references for your topic. This step is integral to ensuring that your essay is backed by credible sources and information, adding depth and authenticity to your work.

• Finally, after selecting your topics and references, hit “Generate Essay.” Lunchbreak AI will take all the information you’ve provided and craft a comprehensive, well-researched essay:

The result:

“Sentiment analysis is a powerful tool that has a significant impact on customer experience management. By utilizing customer data and employing modern text mining approaches, sentiment analysis helps businesses gain insights into customers’ sentiments and emotions. This aids in understanding what customers truly feel and think about their products, services, or overall experience. In the International Journal of Data …, 2019 – igi-global.com article, C Gallagher, E Furey, K Curran investigate the use of sentiment analysis to drive a modern customer experience (Gallagher et al.). The study highlights how sentiment analysis enables businesses to analyze vast amounts of customer feedback from various sources such as social media, online reviews, surveys, and call center recordings. By employing advanced text mining techniques, sentiment analysis tools can accurately identify not just positive or negative sentiments but also the nuances in emotions expressed by customers. This deeper level of understanding empowers businesses to take targeted actions to improve customer satisfaction and address concerns promptly. Sentiment analysis provides valuable insights that allow companies to track trends over time, make data-driven decisions for product improvements or service enhancements, and better understand customers’ needs and preferences. As a result, businesses can tailor their offerings to meet customer expectations more effectively and create personalized experiences that foster long-term loyalty. Overall, sentiment analysis plays a crucial role in enhancing the customer experience by enabling businesses to understand and respond to customers’ sentiments proactively.”

Effective assessment and evaluation tools such as Quizbot.ai are crucial for enhancing learning experiences and measuring progress.

Quizbot.ai emerges as a powerful AI-powered platform designed specifically to address this need. Utilizing advanced natural language processing (NLP) algorithms, Quizbot.ai empowers individuals and organizations to create engaging quizzes, conduct personalized assessments, and gain valuable insights from learner performance data.

Quizbot.ai can be utilized in various settings such as educational institutions, corporate training programs, or even for personal enrichment purposes.

What is Quizbot.ai?

Quizbot.ai is an advanced artificial intelligence-powered platform designed specifically for conducting quizzes and assessments. It utilizes natural language processing (NLP) algorithms to interact with users, enabling them to participate in quizzes and receive instant feedback on their responses. The primary purpose of Quizbot.ai is to enhance the learning experience by engaging users in interactive and educational activities.

One of the key features of Quizbot.ai is its ability to generate personalized quizzes based on the user’s preferences and knowledge level. By analyzing the user’s previous responses, Quizbot.ai adapts its questions to ensure that they are neither too easy nor too difficult. This dynamic approach ensures that users are continuously challenged and can improve their knowledge over time.

In educational institutions, teachers can leverage Quizbot.ai to create interactive quizzes that engage students in a fun and challenging manner. Corporate trainers can use it as an effective tool for assessing employee knowledge during training sessions. Additionally, individuals can benefit from Quizbot.ai by using it as a self-paced learning tool to improve their knowledge in different subjects.

What does Quizbot.ai have to offer?

Quizbot.ai offers numerous benefits for users across various industries. One of the most significant advantages is the enhanced learning experience it provides. The platform allows users to create and customize quizzes, making the learning process more interactive and engaging. This feature is particularly useful for organizations that want to provide their employees or students with a more dynamic way of learning new information.

Another advantage of using Quizbot.ai is that it saves time and effort. The platform allows users to automate the quiz creation process, eliminating the need to manually input each question. This is especially useful for large-scale quizzes, as it significantly reduces the time and effort required to create and administer them.

Quizbot.ai is also designed to be scalable and flexible. Whether you need to create a simple quiz for a few participants or a comprehensive assessment for thousands of learners, the platform can handle it seamlessly. This makes it an ideal solution for organizations that need to train a large number of employees or students.

Personalized assessments are another key benefit of using Quizbot.ai. The platform enables users to tailor quizzes according to individual needs or learning objectives. You can select specific topics, difficulty levels, or even adaptive questioning algorithms that adjust difficulty based on user performance. This feature ensures that each learner receives a personalized learning experience that meets their unique needs.

Finally, Quizbot.ai offers detailed analytics and reporting features that provide valuable insights into learner performance. These insights can help identify knowledge gaps, track progress, and make informed decisions regarding training programs or educational content improvement. This feature is particularly useful for organizations that want to evaluate the effectiveness of their training programs and make data-driven decisions.

Also an AI tool suite

Although its main function is focused on quiz creation, Quizbot.ai also provides users with the following features that they may often need:

• AI Tutors
• AI Images
• AI Code
• Audio to Text
• Text to Audio

And the best part? The site gives you 1,500 words to use in prompts and 5 image generations for free upon registration.

AI Tutors

With AI Tutors, you can use Quizbot AI just like ChatGPT and LLama-2. Trained on a comprehensive database of specific subjects such as calculus, math, English, biology and chemistry, these chatbots are designed to answer anything you have in your academic life.

Using AI Tutors is quite simple. The only thing you need to do is to sign up to Quizbot, navigate to the AI Tutors tab from the dashboard navigation, select your subject and ask what’s on your mind. Quizbot has also added an audio to text tool to the chatbox so that you can ask this question while taking notes.

AI Images

The use of visual memory for training has long been recommended by experts and, unexpectedly, Quizbot can also be used as an AI-image generation tool.

But it may be a while before it’s mentioned as an alternative to Midjourney, DALL-E 3, Stable Diffusion and the newcomer Amazon Titan image generator tool, as it was able to provide this image for our prompt “Tasmanian Devil taking a really hard test in a classroom with other classical cartoon characters”:

AI Code

We also have good news for home-cook coders. Users can use Quizbot just like GitHub Copilot. According to the site, users can use this code generator to create code in any programming language.

Quizbot gave the following result to our prompt “give me a code for a professional and reputable tech blog site” and it looks pretty decent for a startup site:

What type of questions can you prepare with Quizbot.ai?

The platform offers seven different question types, each designed to cater to different learning objectives, topics, and levels of difficulty.

Here’s a breakdown of the question types available on Quizbot:

1. Multiple choice questions (MCQs): These questions consist of a stem or problem statement followed by several options, and the test-taker must select the correct answer from the given choices. MCQs can assess factual knowledge, comprehension, or application skills
2. True/False questions: These questions present statements that are either true or false, requiring the test-taker to determine their veracity
3. Fill in the blanks: In this type of question, a sentence or phrase is presented with one or more missing words, and the test-taker has to fill in the blank(s) with appropriate answers
4. Matching questions: This type involves matching two sets of items that are related in some way. Test-takers need to connect corresponding elements from each set
5. Short answer questions: These questions require concise and specific answers without multiple options or complex explanations
6. Image-based questions: Quizbot also supports image-based questions where an image is provided, and the test-taker must respond based on the visual input

In addition to all that, with Quizbot, you can customize the questions to suit your specific needs and preferences. You can use these question types to create quizzes or assessments for educational purposes, employee training, market research, or any scenario that requires interactive questioning.

The platform’s flexibility allows you to design quizzes that cater to various learning styles and objectives, making it a versatile tool for diverse applications.

Quizbot.ai pricing plans

Quizbot.ai pricing plans are divided into membership and school & company plans. Quiznot has named them with famous scientists:

1. Membership plans:
   • Albert Einstein
   • Marie Curie
   • Issac Newton
2. School & Company plans:
   • Galileo Galilei
   • Ada Lovelace
   • Nikola Tesla

These pricing plans are fine-tuned according to the amount of usage and allow users to spend according to their needs.

You can see the feature differences and costs of the pricing plans in the table below.

With its commitment to continuous improvement and user-centric design, Quizbot.ai is poised to revolutionize the way we assess and learn, empowering individuals and organizations to achieve their educational goals.

Featured image credit: JESHOOTS.COM/Unsplash .

AI tools for teachers have surged in prominence, redefining the educational landscape. Thanks to artificial intelligence, educators can now amplify the impact of their pedagogical methods, ensuring that every moment in the classroom is optimized.

Commencing our exploration, we spotlight some standout AI tools for teachers that promise to be game-changers this 2023. We believe these innovations don’t just augment the teaching process; they also empower students, fostering an environment ripe for enriched learning experiences.

Best AI tools for teachers in 2023

To clarify, the AI tools for teachers we delve into here don’t follow a hierarchical order. Instead, each tool shines in its distinct capacity, allowing educators to select based on specific requirements and preferences.

Magic School AI

Magic School AI stands as a testament to how AI tools for teachers can revolutionize classroom dynamics. It serves as an invaluable assistant, offering educators an array of functionalities to make their roles more efficient and focused. For numerous educators, the allure of Magic School AI lies in its robust Lesson Plan Generator.

Magic School AI is one of the best AI tools for teachers as it features a standout Lesson Plan Generator that simplifies lesson planning and offers various functionalities to make topics more relatable and pertinent to students.

Magic School AI is what teachers need

Think of it: with a few inputs, you have a tailor-made lesson plan replete with objectives, key discussion points, and evaluative measures. By eliminating the time-consuming process of lesson creation, teachers can center their energy on enacting these lessons in the most engaging manner.

But there’s more to this tool. Magic School AI offers behavioral intervention suggestions, adjusts text to cater to diverse grade levels, and even churns out practice SAT papers. Each feature is a step towards ensuring the content is as resonant and beneficial for the students as possible.

Almanack AI

A step into Almanack AI‘s platform immediately reveals its dedication to harnessing AI’s potential to craft superior educational experiences. Unlike many solutions that scratch only the surface of what AI can achieve, Almanack AI delves deeper, employing a suite of AI models to optimize every facet of teaching.

Teachers gather around, Almanack AI is tailored strictly for you

A defining edge of Almanack AI is its intricate understanding of curriculums. It doesn’t just produce resources; it aligns them meticulously with specific learning objectives. So, when a teacher leverages this tool, they’re not just getting AI-powered help; they’re getting assistance that’s calibrated to the highest standards of educational alignment. This precision ensures that what educators receive is not just relevant but also of impeccable quality, minimizing any room for oversight.

Education CoPilot

Tteachers now have a trusty sidekick thanks to Education CoPilot. This AI tool for teachers is akin to a Swiss Army knife, adeptly handling curriculum design, lesson planning, and the tracking of student progress. The beauty of Education CoPilot lies in its ability to individualize learning experiences. Recognizing that each student is unique, it curates bespoke learning paths, ensuring that every learner’s potential is maximized.

Education CoPilot is one of the best AI tools for teachers because it provides a platform for designing tailored lesson plans and activities while tracking student progress, with the added ability to instantly draft teaching materials.

What stands out for many educators, however, is the software’s prowess in instantly generating teaching materials, from handouts to assignments. While the free version itself offers a plethora of features, educators seeking more intricate capabilities, like AI-enhanced templates or extended handout creation, will find the paid version a worthy investment.

SlidesAI.io

Enter SlidesAI.io: a tool that redefines how educators construct their presentations. Harnessing advanced AI algorithms, SlidesAI metamorphoses textual input into visually dynamic slide decks, allowing teachers to prioritize content over design concerns. And it doesn’t stop there. The AI tool astutely recommends imagery and graphics that resonate with the topic, ensuring each slide is not only informative but also engaging.

Integrated effortlessly with Google Workspace, SlidesAI brings forth a range of templates, offering a medley of design options to suit varying pedagogical needs. While it generously provides a wealth of features in its free version, educators who fancy a more polished experience can opt for its affordable premium plans, which introduce benefits like high-definition exports and access to exclusive design templates.

Gradescope

Gradescope is one of the best AI tools for teachers due to its comprehensive grading capabilities, built-in plagiarism checker, and detailed analytics that highlight areas for student improvement.

Among its standout features is an integrated plagiarism checker, ensuring academic integrity is upheld while sparing teachers the need to jump between platforms. But Gradescope isn’t just about marking; it delves deeper, gifting educators with granular analytics that spotlight areas ripe for pedagogical intervention.

The allure of Gradescope’s free version beckons many, but for those seeking an elevated experience, its premium offering packs a punch, boasting custom rubric creation, seamless tool integration, and the collaborative magic of team grading.

PowerPoint Speaker Coach

PowerPoint Speaker Coach has emerged as one of the best AI tools for teachers. It’s designed to empower educators to elevate the impact and engagement of their presentations. Going beyond the superficiality of just creating visually appealing slides, this AI tool dives deep into the nuances of vocal delivery—analyzing a teacher’s pace, tone, and emphasis while they navigate through their PowerPoint slides.

The beauty of PowerPoint Speaker Coach lies in its feedback mechanism. As educators rehearse their lessons, this tool offers actionable insights and recommendations. Whether it’s suggesting a change in tone, advising on better pacing, or emphasizing crucial points, the tool ensures the delivery resonates with the audience. For seasoned educators, it’s an excellent refresher; for novices, it’s a beacon guiding them to perfection.

The crux of education lies in not just what is being taught, but also in how it’s delivered. With distractions aplenty, capturing and retaining student attention is a challenge. PowerPoint Speaker Coach directly addresses this by aiding educators in crafting compelling presentations that keep students’ focus unwavering from start to finish.

Ease of accessibility further amplifies its appeal. Teachers can access this invaluable tool directly within the PowerPoint web app. A simple click on “Slide Show” followed by “Rehearse with Coach” unlocks a world of guidance and refined presentation skills. In embracing such AI tools for teachers, educators are not just enhancing their own teaching techniques but also enriching the overall classroom experience for their students.

QuillBot

QuillBot, an AI maestro that transforms text in a blink. Teachers, often pressed for time, find in QuillBot an ally to swiftly craft lesson materials, worksheets, and more. The premise is simple: feed it a sentence or paragraph, and watch as it serves up varied renditions, each preserving essence but flaunting fresh phrasing.

QuillBot is one of the best AI tools for teachers as it aids in swiftly generating diverse content from existing materials, making it a handy tool for lesson creation, while also supporting language learners in expressing their ideas more clearly.

Beyond its core, QuillBot doubles as a linguistic coach for students, especially those mastering a language. By showcasing diverse ways to articulate thoughts, it amplifies students’ expressive capacities. And for teachers, the tool’s added functionalities, like grammar checks and citation aids, ensure that content produced stands tall in quality and integrity.

QuillBot’s basic plan already offers a rich palette, but its premium offering, priced at a modest USD 9.95 monthly, elevates the experience with advanced features, like nuanced grammar rewrites and plagiarism checks. Thus, whether on a budget or seeking the best, QuillBot promises to be an invaluable addition to the educator’s toolkit.

Queirum

Embarking on a quest to bolster students’ prowess in the realm of STEM, Queirum presents an AI-driven platform tailored to hone critical skills. As students set their sights on college and future careers, Queirum ensures they’re armed with the requisite knowledge and aptitude.

Queirum is one of the best AI tools for teachers because it focuses on mastering critical STEM skills and offers a virtual AI tutor that can analyze student performance in real-time, giving insights to educators about areas needing improvement.

At the heart of this educational haven is the AI virtual tutor, proving its mettle in enhancing the depth and pace of learning. Not just that, it’s evident in the measurable uptick in student outcomes. But Queirum isn’t merely student-centric. Teachers too find immense value as the AI meticulously scrutinizes answer patterns and the duration of tutorial sessions, thereby offering insights into each student’s learning trajectory.

Plaito

Imagine a virtual coach, always by a student’s side, guiding them through the labyrinth of writing, debating, and collaborating. That’s Plaito, an AI beacon bringing the intimacy of personalized tutoring to every learner, ensuring that their educational journey is marked by clarity, confidence, and empowerment.

Plaito is one of the best AI tools for teachers as it merges the efficacy of AI with language science to craft lessons at just the right pace for students and boasts features like game-like challenges and multi-language support.

With Plaito, students have a unique proposition: snap a picture of their homework, and voila, the AI seamlessly tutors them, transforming challenges into learning opportunities. The amalgamation of cutting-edge AI and linguistic science ensures that lessons are meticulously calibrated to each student’s pace and level. But it’s not all work and no play; Plaito, akin to a friendly companion, engages in chats, enhancing the learning experience in multiple languages.

Fetchy

Fetchy emerges as an AI beacon, meticulously crafted for the educator’s world. Its mission is clear: amplify the educator’s prowess in teaching by effortlessly navigating through the myriad tasks they encounter daily. Whether it’s sculpting engaging lessons, fashioning newsletters, or drafting polished emails, Fetchy is the go-to companion.

Fetchy is one of the best AI tools for teachers because it streamlines various educational tasks, from crafting lessons to generating newsletters, allowing educators to optimize their time and teaching methods.

One of Fetchy’s standout attributes is its adeptness in tailoring language, catering specifically to an educator’s needs. By sidestepping the intricacies of elaborate prompts, Fetchy assures educators of its readiness at their beck and call. The promise is straightforward: every interaction with Fetchy yields results that are in tune with the educator’s distinct educational demands.

Formative AI

Formative AI stands tall as an astute ally for educators. It’s akin to having an insightful co-teacher that provides real-time feedback on students, spotlighting their strengths and areas needing attention. This ensures teachers are equipped with actionable insights, paving the way for more effective pedagogical strategies.

Formative AI is one of the best AI tools for teachers due to its real-time feedback mechanism on student performance, and its ability to personalize learning experiences, making assessment creation and grading more efficient.

Formative AI’s repertoire is vast, offering an assortment of assessment methods – from multiple-choice to image-centric questions. Educators, be it novices or veterans, will find the tool’s interface intuitive, with the option to either craft assessments from ground-up or employ pre-existing templates.

A salient feature that positions Formative AI in a league of its own is its commitment to personalized learning. Through sophisticated AI algorithms, it meticulously analyzes student submissions, delivering feedback that resonates with each learner’s unique journey. And the cherry on top? It’s absolutely free.

Cognii

Hailing from Boston, Cognii is at the forefront of sculpting AI-driven solutions for the educational spectrum, spanning from K-12 to higher echelons and even corporate training landscapes.

Cognii is one of the best AI tools for teachers as its virtual learning assistant leverages conversational technology to bolster open-format responses and critical thinking in students, alongside providing real-time, personalized feedback.

Cognii’s crown jewel is the Virtual Learning Assistant, anchored in cutting-edge conversational technology. This assistant ventures beyond traditional learning mechanics, encouraging students to craft open-ended responses, thus sharpening their critical faculties.

In tandem with this, the virtual aide offers personalized tutoring, ensuring feedback is tailored, resonating with the nuances of each student’s learning path.

Final remarks

In light of the swift progression of AI in the educational sector, AI tools for teachers have transitioned from being an innovative luxury to an indispensable component of contemporary education. These tools are not merely altering; they are revolutionizing the dynamics of how educators formulate lessons and engage with their students.

As 2023 unfolds and we advance into its latter half, it’s paramount for educators to leverage these AI tools for teachers. By integrating these advanced tools, they can ensure they are not just keeping pace with the evolving educational landscape but also providing an enriched, state-of-the-art learning experience for their students.

Featured image credit: Kerem Gülen/Midjourney

Emerging trends in educational technology have consistently served as pivotal points of transformation in the education sector. Right from the days of the printing press to the current era of online learning, technological advancements have revolutionized the way we assimilate knowledge. As we navigate through 2023, it’s evident that emerging trends in educational technology are setting new standards and reshaping the educational terrain.

In this piece, we delve into some of the significant technological advancements that are setting the tone for 2023. Notably, the ascent of artificial intelligence, the growing reliance on digital tools, and the innovative shifts in our learning methodologies highlight the dynamic landscape of education.

10 emerging trends in educational technology

Educational technology is reinventing the interactions between students, educators, and academic establishments at large. As we anticipate the future, even more impactful and beneficial trends within the educational technology domain are surfacing, paving the way for holistic and enriched learning spaces.

Emerging trends in educational technology continue to redefine the educational landscape. Here are some of the latest trends we’ve spotted for 2023:

Ubiquitous learning through mobile and cloud platforms

Post the Covid-19 pandemic, e-learning platforms have gained significant traction, enabling students to tap into high-caliber educational content and educators from any corner of the globe. These platforms not only proffer a vast spectrum of resources for learners and educators but also reshape our engagement with educational materials, amplifying the digital shift in education.

Smart slassrooms with AI-driven solutions

Classrooms embedded with AI features, like facial recognition, natural language processing, and machine learning, are morphing into more interactive and student-friendly spaces. Such AI-enhanced environments craft personalized learning experiences and grant educators the flexibility to mold their instruction to individual student profiles. The integration of AI into the education realm promises profound changes in the imminent years.

Immersive worlds through AR and VR technologies

Harnessing the power of AR and VR, education is taking a quantum leap into the realms of immersive learning. These technologies offer students the thrill of navigating through virtual terrains, executing tasks, and participating in simulations molded to their unique requirements. As these tools permeate the mainstream, their educational influence will undeniably deepen.

Learning gamification

The strategy of embedding game design elements in academic settings, known as gamification, is garnering widespread adoption. Initiatives like accruing virtual points upon task completion or indulging in friendly competition via digital leaderboards exemplify this trend. By infusing the essence of play into the learning curve, students can assimilate information more effectively, relishing an interactive educational journey.

EdTech meets wearables

The surge in wearable technology promises to revolutionize learning environments. From tracking progress and offering real-time feedback to enabling learners to engage with audio lectures or jot down voice notes, wearable devices like smartwatches and VR headsets are making academic pursuits both convenient and interactive. Beyond students, educators and parents stand to gain immensely from these advancements, underscoring a more accessible and streamlined learning experience.

The dawn of automated assessments

Automation, one of the emerging trends in educational technology, is set to streamline the realm of assessments. Advanced automated tools will dominate, offering educators keen insights into student performance and areas requiring attention. For students, these tools can shine a light on their weak points, nudging them to refine specific skills. Furthermore, automated grading systems promise a quicker and more precise evaluation, freeing up educators from the tedium of manual grading.

The rise of adaptive learning

Adaptive learning techniques are emerging as game-changers, focusing on molding courses to suit the distinctive requirements of every student. By leveraging data analytics, this progressive trend allows for the design of bespoke learning experiences. This not only aids educators in catering to a heterogeneous student body but also empowers learners by offering tailored learning trajectories and pacing.

Doctrina AI can easily be your best study ally

Cloud computing in education

Cloud computing, already a stalwart in many industries, will further solidify its presence in the educational domain. This technology negates the need for costly textbooks, ushering in an era where cloud-based materials are at one’s fingertips from any location. As an intrinsic part of the emerging trends in educational technology, cloud computing emphasizes robust authentication to safeguard data while promoting seamless collaboration among academic stakeholders.

Learning in the age of posts

Social media, with its ever-growing influence, is undeniably altering our learning paradigm. It has opened up avenues for learners globally to interact, access, and disseminate information. For educators, social media platforms have become instrumental, serving as tools to enhance reach and elevate student engagement. Though we’re witnessing just the dawn of social media’s potential in education, its transformative impact is evident. As we move ahead, expect these platforms to continually reshape the dynamics of teaching and learning.

The surge of mobile learning

The ubiquity of mobile devices in the educational sphere is undeniable. With an increasing number of learners relying on smartphones and tablets, educational content is rapidly evolving to cater to this mobile audience. Devices that fit in our pockets are gradually overshadowing conventional learning tools, championing a narrative where learning is not confined to classrooms but happens everywhere. Thanks to mobile-centric e-learning solutions, learning has truly become a continuous journey.

The future of EdTech

The trajectory of educational technology over the past decade has been nothing short of transformative. As we step into 2023, these emerging trends in educational technology promise to wield an even more profound influence on classrooms, curriculums, and pedagogical approaches. It’s crystal clear that edtech is not just a passing phase but an integral part of the modern classroom’s DNA.

Featured image credit: Kerem Gülen/Midjourney 

The COVID-19 pandemic has changed the way education is imparted, with Virtual Learning Environments (VLEs) becoming an essential part of the educational landscape. As a consequence of the global pandemic caused by COVID-19, universities and educational institutions worldwide had to swiftly adapt to remote teaching, necessitating the integration and extensive use of technology in their pedagogical approaches. VLEs emerged as a viable solution to ensure continuity in education during the confinement periods, allowing students to engage with their coursework and interact with instructors from the safety of their homes.

During the second semester of the 2019–2020 academic year, the Faculty of Education Sciences at the University of Granada conducted a study to assess students’ perceptions of the pedagogical model adopted in the virtual learning environment during confinement. The research utilized an online questionnaire to gather data and revealed a widespread sense of dissatisfaction among students. Key areas of concern included methodological development, professor involvement, and students’ familiarity with various technological tools and platforms. Despite these challenges, VLEs have presented numerous positive sides that have impacted the way education is imparted and experienced.

In this context, it is essential to explore the positive aspects of VLEs. The integration of virtual learning environments during the pandemic has led to several significant advantages for the education system. These benefits include the continuity of education, flexibility, and accessibility for students, diverse learning resources, collaborative learning opportunities, personalized learning experiences, increased integration of technology, global reach and international collaboration, cost-effectiveness, and positive environmental impact. Embracing VLEs has paved the way for a more dynamic, inclusive, and technology-enhanced future of education.

Understanding the concept of a Virtual Learning Environment (VLE)

At its core, a Virtual Learning Environment refers to a digital space where educational activities and interactions occur through the use of various technologies and tools. These environments create an online space where students can access learning materials, collaborate with peers, and engage with educational content in a flexible and personalized manner.

VLEs are designed to complement traditional teaching methods and cater to diverse learning needs, making education accessible to a broader audience regardless of geographical constraints or physical abilities.

Key features of a Virtual Learning Environment

Virtual Learning Environments offer a wide array of features that enhance the learning experience for both students and educators.

Some of the key features include:

• Content and resources: VLEs host a plethora of educational content, including multimedia resources, e-books, lectures, and supplementary materials, empowering students to learn at their own pace and revisit concepts whenever needed
• Learning objectives: Clear learning objectives are defined within the VLE, guiding students towards achieving specific educational goals
• Interactivity: Virtual Learning Environments facilitate active learning through interactive quizzes, discussions, and virtual simulations that stimulate critical thinking and problem-solving skills
• Learning materials: Instructors can upload and organize learning materials, fostering a structured and engaging learning experience for students
• Accessibility: VLEs break down barriers to education by offering accessibility features that cater to individuals with diverse learning needs
• Learning paths: Students can choose personalized learning paths that align with their interests and learning preferences, promoting a sense of autonomy in the learning process
• Collaboration opportunities: VLEs enable students to collaborate with peers, enhancing teamwork skills and encouraging cross-cultural interactions
• Learning analytics: Educators can analyze student performance and engagement data to identify areas of improvement and offer personalized feedback
• Gamification elements: Gamified elements, such as badges and rewards, motivate students to actively participate in their learning journey
• Learning technology tools: VLEs integrate a variety of educational tools like video conferencing, virtual whiteboards, and collaborative document editors, enriching the online learning experience

The difference between a Virtual Learning Environment and a traditional classroom

Traditional classrooms and Virtual Learning Environments (VLEs) are two distinct approaches to imparting knowledge, and they present contrasting features that cater to diverse learning needs. In a traditional classroom setting, the hallmark is face-to-face interactions between students and teachers. The physical presence of instructors facilitates immediate and direct communication, enabling real-time engagement and feedback. On the other hand, VLEs primarily rely on digital communication and collaboration tools, enabling learners to participate in educational activities remotely, without being constrained by geographical barriers.

One of the key differentiators between traditional classrooms and VLEs is the flexibility they offer to learners. Traditional classrooms have set schedules and fixed timetables, leaving little room for students to adapt their learning process according to their individual pace and preferences. In contrast, VLEs provide students with the freedom to access learning materials and coursework at any time, allowing them to structure their learning journey in a manner that best suits their needs. This flexibility is particularly advantageous for adult learners who may have work or family commitments that demand a more adaptable learning environment.

Furthermore, VLEs transcend physical boundaries and open up a world of possibilities for learners from diverse backgrounds and locations. The virtual space of VLEs fosters a global learning community where students from different countries and cultures can come together and collaborate. This intercultural exchange enriches the learning experience and broadens students’ perspectives, preparing them to thrive in a connected and globalized world.

Research has shown that VLEs have gained significant popularity among learners due to several factors. The low costs associated with online courses and the ability to access content from anywhere and at any time make VLEs a compelling choice for many. Additionally, the integration of modern technology and the use of social media within VLEs create engaging and interactive learning environments that resonate with today’s tech-savvy learners.

In traditional classrooms, the role of teachers extends beyond imparting knowledge; they also serve as mentors and guides. The direct interaction between teachers and students allows educators to understand their students better, identify their strengths and weaknesses, and provide personalized support to facilitate learning. Classroom settings also encourage peer-to-peer interactions, fostering a sense of community and collaboration among students.

On the other hand, VLEs have led to a transformation in teachers’ roles. With the shift from teacher-based learning to technology-based learning, instructors in VLEs adopt a more facilitative role, providing mentorship and support to students in their autonomous learning journeys. VLEs rely on various communication channels, such as online forums and chatrooms, to address students’ queries and provide assistance. While some argue that direct interaction with teachers in traditional classrooms enhances the learning experience, synchronous online courses cater to those who prefer real-time engagement with instructors.

How AI improves education with personalized learning at scale and other new capabilities

Both traditional classrooms and VLEs have their advantages and disadvantages, and the choice between the two depends on various factors such as the learners’ age, preferences, and goals. Young children and adolescents may benefit from the social and interactive aspects of traditional classrooms, while working professionals and adult learners may find the flexibility and convenience of VLEs more appealing. As technology continues to evolve, VLEs are expected to play an increasingly vital role in shaping the future of education, offering dynamic, accessible, and globalized learning experiences for learners of all ages and backgrounds.

How Virtual Learning Environment is changing the face of education

Virtual Learning Environments (VLEs) have ushered in a transformative era in education, revolutionizing traditional educational approaches. The accelerated adoption of VLEs during and after the COVID-19 pandemic has reshaped learning, breaking down barriers and redefining the possibilities of formal education. A significant advantage of Virtual Learning Environments is their ability to bridge geographical gaps, enabling individuals from remote areas or different countries to access quality education that they might have otherwise been excluded from. This inclusivity fosters a more diverse and globally connected learning community, enriching the educational experience for all participants.

One of the remarkable features of Virtual Learning Environments is their adaptability to cater to the diverse learning needs of students. These environments enable personalized learning experiences, taking into account each individual’s unique style and pace of learning. The flexibility of VLEs ensures that learners can access educational resources and coursework at their convenience, empowering them to take charge of their educational journey. Moreover, educators can provide tailored support and guidance, ensuring that no student is left behind, and all learners have the opportunity to thrive.

VLEs are also characterized by their data-driven nature, which offers valuable insights into student performance and engagement patterns. Through learning analytics, educators can monitor students’ progress, participation, and achievement, allowing for evidence-based strategies to optimize the learning process. These insights empower instructors to identify areas of improvement and make informed decisions on educational methodologies and interventions.

To further incentivize student engagement, Virtual Learning Environments often integrate gamification elements into the learning experience. Gamification elements, such as rewards, challenges, and leaderboards, create a sense of achievement and motivation among students, encouraging active participation and progress in their educational journey. The gamification approach harnesses students’ intrinsic motivation and fosters a positive learning environment.

Research on VLEs has indicated their potential to positively impact student achievement and involvement. A study investigating the correlation between scholastic achievement, involvement, and satisfaction with VLEs among undergraduate students found that students’ contentment with their virtual learning environments was substantially associated with their scholastic achievement and level of involvement. This highlights the importance of well-designed and implemented Virtual Learning Environments in fostering positive learning outcomes and student engagement.

Looking into the future of eLearning, technology will continue to play a pivotal role in shaping the learning experience. Advancements such as eLearning apps and tutor booking apps will further enhance accessibility and learning opportunities. Virtual classrooms, supported by advanced Learning Management Systems (LMSs) and learning analytics, will enable interactive and immersive learning experiences.

The integration of augmented reality (AR), virtual reality (VR), and machine learning will promote scenario-based learning, where students can actively engage in practical exercises and simulations, leading to deeper comprehension and retention of knowledge. Decentralized eLearning platforms will offer greater transparency and autonomy to educators and students, providing more control over content creation and access to educational resources.

Featured image credit: Image by Freepik.

Artificial intelligence in education promises to disrupt traditional education models and elevate the educational process through cutting-edge teaching and assessment techniques. It’s a world where robots coexist with humans, technology has become an integral part of our daily lives, and artificial intelligence is changing how we learn and educate.

Just like in the classic science fiction movies of the past, AI has become a reality in the present, transforming education and making the future seem like a nostalgic dream come true. From personalized learning experiences to efficient assessment and increased accessibility, AI is paving the way for a new era of education, and it’s time to explore the opportunities and challenges that come with this exciting innovation. So buckle up and join us on a journey through the world of AI in education as we explore how this technology shapes the future of learning.

Introduction to artificial intelligence in education

Artificial intelligence is transforming virtually every aspect of our lives, and education is no exception. With AI-powered tools and platforms becoming increasingly prevalent in the classroom, educators, administrators, and policymakers need to understand the emerging landscape of artificial intelligence in education.

This article provides an overview of the most pressing and exciting developments in this field, exploring the impact of AI on learning and teaching and delving into the benefits and challenges posed by this rapidly evolving technology. Whether you’re an educator looking to incorporate AI into your practice or a policymaker interested in shaping the future of education, this article provides essential insights into the transformative potential of AI. So, let’s dive into the world of AI in education and discover how this powerful technology is shaping the future of learning.

The accelerated adoption of AI and online education in the wake of the COVID-19 pandemic

The COVID-19 pandemic has profoundly impacted the global education landscape, triggering a rapid shift away from traditional, in-person education towards remote and AI-powered learning solutions. With schools and universities forced to close their doors, educators and students alike have rapidly adapted to a new reality where technology plays a central role in delivering education.

This has resulted in a surge in demand for online education platforms and AI-powered tools as institutions scramble to ensure that learning can continue uninterrupted. The pandemic has accelerated the transition towards technology-driven education, highlighting the importance of investing in AI and online learning solutions and emphasizing the need for institutions to be prepared to pivot quickly in the face of unexpected challenges. As the world continues to navigate the aftermath of the pandemic, it is clear that the integration of AI and online education is here to stay and that it will play an increasingly important role in shaping the future of education.

The importance of artificial intelligence in education

Artificial intelligence has the potential to revolutionize the way we learn and teach, offering unprecedented opportunities to improve education outcomes and support student success. From personalized learning experiences to cutting-edge assessment tools, AI is already changing the face of education in profound ways.

The advantages of artificial intelligence in education

But why is AI so important in the field of education? Here are a few key reasons:

Personalized learning

AI can analyze large amounts of data about each student’s learning preferences and habits, enabling educators to create customized learning experiences that cater to each student’s unique needs and abilities.

Improved assessment

AI-powered tools can provide instant feedback on student performance, allowing educators to assess and adjust their teaching methods in real-time.

Access to quality education

AI can help bridge the gap between students in under-resourced areas and their more privileged peers by providing access to quality educational resources and opportunities.

Increased efficiency

AI can automate repetitive tasks and streamline administrative processes, freeing educators to focus on what they do best: teaching.

Artificial intelligence is both Yin and Yang

The disadvantages of artificial intelligence in education

While the potential benefits of artificial intelligence in education are vast, it is also important to acknowledge the potential challenges and drawbacks. Here are a few key disadvantages to consider:

Job losses

AI has the potential to automate many jobs, including those in the education sector. While this could lead to increased efficiency, it may also result in job losses and economic insecurity for educators.

Dependence on technology

As we become more reliant on AI tools and platforms, we may become less able to solve problems and think critically without the support of technology.

Bias and discrimination

AI systems are only as unbiased as the data they are trained on. If the data contains biases or discriminatory information, the AI system may perpetuate these biases in its decisions and recommendations.

Lack of human interaction

AI-powered tools and platforms may reduce opportunities for human interaction and collaboration in the classroom, which can be critical components of a well-rounded education.

Privacy concerns

The use of artificial intelligence in education requires large amounts of student data, raising questions about privacy and the security of sensitive information.

Examples of artificial intelligence in education: Tools and platforms for the classroom

The world of artificial intelligence in education is rapidly evolving, and new tools and platforms are emerging all the time. Here are a few examples of how AI is already making a difference in the classroom:

• Personalized learning platforms: AI-powered platforms like Knewton and Carnegie Learning use data and algorithms to provide personalized learning experiences for each student, offering customized recommendations and feedback.
• Adaptive assessment tools: AI-powered tools like Gradescope and Kaltura provide instant feedback on student performance, allowing educators to assess and adjust their teaching methods in real time.
• Virtual tutors: AI-powered virtual tutors like Querium and Carnegie Learning’s ALEKS can provide students with 24/7 access to high-quality educational resources and support.
• Speech and language tools: AI-powered tools like Cognii are helping students develop their language and communication skills, offering real-time feedback and personalized learning experiences.
• Automated grading: AI-powered tools like Gradescope and Coursera can automate the grading process, freeing up educators to focus on other aspects of teaching and learning.

The future of artificial intelligence in education: Opportunities and challenges

Opportunities:

• Personalized learning: AI algorithms can provide each student with a customized learning experience, tailoring content and assessments to meet their individual needs and abilities.
• Improved assessment: AI-powered tools can automate the grading process and provide instant feedback, allowing educators to focus on teaching and student development.
• Increased accessibility: AI-powered tools and platforms can provide students with access to high-quality educational resources and support, regardless of their location or financial status.
• Enhanced efficiency: AI-powered tools can streamline administrative tasks and reduce grading time, freeing educators to focus on other aspects of teaching and learning.
• New learning experiences: AI-powered tools and platforms can offer immersive, interactive learning experiences that engage students and increase motivation.

Challenges:

• Bias and fairness: There is a risk that AI algorithms may perpetuate or amplify existing biases in the education system, particularly if the algorithms are trained on biased data.
• Privacy and security: As more sensitive student data is collected and stored, there is a growing concern around privacy and security and the potential for data breaches or misuse.
• Economic and social implications: The widespread adoption of AI in education may lead to job losses and other economic and social implications, particularly for educators and support staff.
• Technical limitations: AI-powered tools and platforms may not be accessible to all students, particularly those who lack access to technology or the internet.
• Ethical considerations: There is a need to address ethical considerations around the use of artificial intelligence in education, particularly around issues such as data privacy and the role of technology in shaping student development.

Ethical concerns in the use of artificial intelligence in education

The use of artificial intelligence in education raises a number of important ethical considerations. These concerns center around issues such as data privacy, student well-being, and the role of technology in shaping student development.

One of the key ethical concerns is data privacy. AI-powered tools and platforms collect and store large amounts of student data, including sensitive information such as grades, test scores, and personal information. There is a risk that this data could be misused or that unauthorized individuals could access it. This highlights the need for robust privacy policies and security measures to protect student data.

AI and Ethics: Balancing progress and protection

Another ethical concern is the impact of AI on student well-being. AI algorithms can provide students with instant feedback and assessment, which can benefit student learning. However, if not used correctly, AI algorithms can also increase stress and anxiety, particularly if students focus on achieving high grades and meeting certain performance targets. This highlights the need for caution when using AI algorithms in the classroom and the importance of considering the impact of these tools on student well-being.

Finally, there is a concern about the role of technology in shaping student development. AI algorithms can provide students with personalized learning experiences and tailor content to their individual needs and abilities. However, there is a risk that students will become overly reliant on AI tools and miss out on important social, emotional, and cognitive skills developed through human interaction and experience. This highlights the need for balance when using AI in education and the importance of ensuring that students have opportunities to develop a full range of skills and competencies.

The use of artificial intelligence in education raises a number of important ethical considerations, and educators and policymakers must address these concerns head-on. This can be achieved through robust privacy policies and security measures, caution when using AI algorithms in the classroom, and a focus on ensuring that students have opportunities to develop a full range of skills and competencies. By addressing these ethical concerns, we can ensure that the use of artificial intelligence in education is both effective and responsible.

Final words

Integrating sophisticated technology into formative assessments can significantly augment the efficiency and effectiveness of the teaching and learning process. The utilization of advanced AI-powered mechanisms, such as intelligent tutoring systems, covert evaluations, interactive games, and simulated realities, presents a multitude of opportunities for creating engaging, interactive educational tools. However, to fully realize these benefits, the education sector must prioritize investment in the research and development of cutting-edge testing technologies that can furnish teachers and students with the necessary tools to achieve their educational goals.

Caktus AI writer promises to free you from endless homework! Caktus AI is not here to compete with the big names of AI writers but to be the best buddy of educators everywhere. Caktus AI is a company that uses AI to rethink the classroom experience. According to the firm, artificial intelligence can help educators personalize lessons to each student, boosting students’ motivation to learn and the quality of their outcomes.

Caktus AI is creating the first AI-powered learning platform that can adapt to the unique requirements of each student based on their individual needs and preferences. In addition, its platform can adapt to each individual’s changing demands over time, ensuring that they always receive the best possible specialized and efficient education. How does it work? Keep reading and find out.

What is Caktus AI writer?

Caktus AI is the first artificial intelligence technology designed specifically for use in classrooms. All of your schoolwork can be completed automatically, and Caktus AI writer is ready to deliver your essays in minutes. Is it just limited to essays? No, not at all! Caktus AI writer’s versatile features allow for writing code, cover letters, CV bullet points, and more. It is quite handy, right?

@caktus.ai

caktus.ai is here to help you be the most efficient student you can!🌵

♬ use this audio if im the best editor oat – alpine

In addition, to be an AI-powered learning platform, the Caktus AI team is developing a wide range of new AI-based tools and applications to support educators in the classroom. Chatbots that can answer students’ inquiries are only one example of the variety of resources available to them.

We believe that AI has the potential to transform education, and we are committed to building the tools and applications that will make this possible.

Caktus AI team

Caktus AI gives you nearly infinite quantities of credits to your account as soon as you join the platform, whereas many AI platforms have a daily limit for free use.

Check out the OpenAI ChatGPT chatbot; people have already fallen in love with it!

How to use Caktus AI writer?

Just follow these steps:

• Go to the link.
• Give your necessary information.

• Click Signup.
• Choose a feature you need, such as a paragraph generator, personal statement writer, etc.
• Finally, give your prompt and generate.

Using Caktus AI is simple; head over to the Caktus AI website and click the “Create New Essay +” button on the top left of the page. Your browser will open to the registration screen, where you’ll be prompted to provide your name, e-mail address, and password for accessing the site.

See below for a rundown of the Caktus AI capabilities that can be used in your classroom:

• For writing
  • Blog post ideas
  • Blog post outline
  • Text summarizer
  • Paragraph generator
  • Creative story
  • Content improver
  • Essay writer

• • Personal bio for LinkedIn
  • Persuasive bullet points
  • Persuasive bullet points
  • Hook generator
  • Sentence expander
  • Email writer
  • Grammar fixer
  • Article summarizer
  • AP style formating
  • Email responder
  • Poetry writer

• • Scriptwriter
  • Conclusion writer
  • College Apps writer
  • Discussion questions
  • Discussion board post (Disagree)
  • Discussion board post (Agree)
  • Book summarizer
  • Ted talks to essay

• • Personal statement writer
  • Youtube to essay

• For coding:
  • Python writer
  • Bash code creator
  • C# writer
  • Go writer
  • Java writer
  • Javascript writer
  • Ruby writer
  • Rust writer
  • SQL writer
  • Typescript writer

• • Write a Python docstring
  • JavaScript to Python
• For studying:
  • Text to notes
  • Article note taker
• For language learning:
  • Spanish tutor
  • Mandarin answerer
  • Italian tutor
  • Russian tutor
  • Japanese tutor
  • French tutor
  • Arabic tutor
• STEM:
  • Present value
  • Dervaitive calculaltor
  • Integral calculator
  • Chemical analyzer
  • Reaction balancer
  • Geometric shifter
  • Geology tutor
• For job applications:
  • Cover letter writer
  • Resume bullet points
• For fun:
  • Class absent excuses
  • Love letter
  • Songwriter
  • Caption writer
  • Text responder
• For arts:
  • Movie Scene Analysis
• For math & science:
  • Problem solver

Caktus AI will give you 20,000 free credits. But what does happen when you run out of them?

Check out the best free AI art generators

Pricing plans

Caktus AI provides two options, both of which provide unlimited usage and cutting-edge AI benefits.

1. $9.99 monthly premium plan
2. $59.99 yearly premium plan

The benefit you receive from both plans is identical. The following are some of the highlights of the offerings:

• Unlimited credits
• Advanced AI that lets you write better Essays and Codes
• 20+ Templates for studies and Flashcards
• 100% Unique content generation

When you join Caktus AI, they will immediately add 20,000 credits to your account, which can be utilized in any of the aforementioned ways. Each phrase in the solutions you generate in the tools will cost one credit. Thus, you might easily use them all up in a flash. That’s why they included a recommendation system for people to use when joining the platform. When a buddy joins Caktus AI, thanks to your invitation, you both receive 10,000 credits.

Verdict

Caktus AI, which focuses on education rather than doing everything like ChatGPT, appears to be the most useful tool for both students and teachers.

The platform solved our complicated programming and math problems in seconds, which was a pleasant surprise. We also appreciated that brand-new users were heavily rewarded for their efforts in beta-testing the service. Caktus AI is being used by 132,070 students already.

We encourage both students and educators to give Caktus AI a try.

Are you wondering how your room will be in cyberpunk style? Try Interior AI

Caktus AI alternatives

Even while not all of these programs were designed with education in mind, you can still use them to convert text to text for use in your studies.

• Meta’s Galactica AI
• Notion AI
• Chai
• NovelAI
• ChatGPT

Other AI tools we have reviewed

We have already explained some of the best AI tools like Uberduck AI, MOVIO AI, Make-A-Video, and AI Dungeon. Do you know there are also AI art robots? Check the Ai-Da.

Are you into AI image generation? You can try these tools:

• MyHeritage AI Time Machine,
• Reface app
• Dawn AI
• Lensa AI
• Meitu AI Art
• Stable Diffusion
• DALL-E 2
• Midjourney
• DreamBooth AI
• Wombo Dream
• NightCafe AI
• QQ Different Dimension Me

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence.

The latest initiative aims to use tools to provide content curation with AI in order to make education more accessible to everyone. Every one of us has had to endure the horrible prospect of seeing an extended video of a presentation, college lecture, or business “fireside chat.” The COVID-19 lockdown, which has transformed many of us into groggy remote workers and learners, has only served to accelerate this trend.

Advantages of content curation with AI

Finding relevant high-quality information from other sources and promoting it to increase your authority and engagement is the goal of content curation. Curation may be automated using AI and machine learning, much like content generation.

Content curation with AI not only assists in content selection, but these tools also customize the result for each contact depending on their specific preferences. Education is a key concept for every nation, you can also learn how could AI transform developing countries in our article.

Every day, businesses and educational institutions produce hundreds of millions of hours of long-form video, but because editing and production costs can be astronomical, the majority of us are unable to fully benefit from these productions.

To resolve this paradox, Educational Vision Technologies (EVT) was founded. To do this, EVT has created a pair of services that make long-form video content more palatable for knowledge workers and university students. They utilize machine learning and tools that provide content curation with AI. These services use machine learning algorithms to divide videos into manageable chapters of just a few minutes each, each of which is accompanied by a full transcript and other notes.

The program, which was first designed with the needs of students with disabilities in mind, also helps abled students who are unable to attend class or who find the note-taking aid useful, as well as knowledge and other remote employees. EVT’s founder and CEO, Monal Parmar, observes:

“Studies have shown that it takes more cognitive effort to take notes while trying to listen to a lecture than it does to play chess. It doesn’t make sense for students to overexert their cognitive bandwidth to write everything from the whiteboard or chalkboard. Providing notes gives students the flexibility to take as few or as many notes as work best for them.”

According to research from Kansas State University, students who are using “externally provided lecture notes… generally achieve more on exams than do learners who review their own notes.”

A number of University of California San Diego (UCSD) departments, a few other universities, and a professional training organization all use the EVT service.

Joanna Boval, head of the UCSD office for students with disabilities, writes in a letter that students with disabilities “participate in an academic lecture numerous times and at the student’s individual pace.”

According to Parmar, EVT has raised $700,000 and presently employs four full-time staff members and three contractors.

A number of sales prospects, according to him, have resulted from the company’s participation in the Oracle for Startups program, which was used to build the company’s service on Oracle Cloud Infrastructure (OCI). He clarifies:

“Many cloud accelerator programs are just called accelerators in name but do little more than provide cloud credits and some technical guidance. Oracle’s does a lot more than that.”

EVT is powered by ML

EVT provides its services in two different forms. Customers can upload their own movies for content curation on EVT Bloom, while EVT Learning Systems employs a device that is located on-site.

EVT Bloom automatically generates an interactive table of contents, a searchable voice transcript, speaker summaries, and quiz questions by segmenting recordings into concise video chapters using machine learning. On EVT’s web platform, the titles of the brief videos are put as headers so that a screen reader may read them aloud and help those who are blind or visually impaired access the video material more easily.

In order to grow the service, the business faced a variety of machine learning hurdles, according to Parmar. It also had to deal with hardware flaws including overheating, unstable power supplies, networking problems, and supply chain disruptions on the on-premises device utilized by EVT Learning Systems. The software product, however, had a more simplified development process.

“We simply transferred many of our core algorithms to the cloud and turned them into microservices,” said Parmar.

The price of EVT Bloom subscription plans, which are invoiced annually, ranges from $33 per hour of video to $60 per hour. According to Parmar, AI-curated content for one-hour courses is ready in 30 minutes, and completely corrected versions in 24 hours.

Although UCSD has long been utilizing technology to advance its objective of delivering fair and inclusive learning to a diverse student population, the COVID-19 epidemic hastened the acceptance of it there. Its Chancellor Pradeep Khosla said in a statement:

“They have helped us not only improve accessibility, but also improve student satisfaction for distance learning.”

Oracle has its own solutions

EVT Bloom processing takes place on OCI, and the content is secured and kept in OCI Object Storage. Parmar clarifies:

“We developed our machine learning microservices on OCI. Over many meetings, Oracle’s engineers guided us to prototype and develop our machine learning microservice infrastructure in the Oracle Cloud. For any technical challenge, Oracle engineers have been ready to support us and regularly check in to see how things are going.”

Given that EVT offers hundreds of educational videos that are broadcast from its website, Parmar observes that cloud expenses could have been a deciding issue for his business. But because the Oracle content curation service doesn’t charge egress for the first 10TB per month, it was able to significantly reduce the cost of video streaming. It is also important to discuss how Ethical AI should be.

When the pandemic struck, Parmar had just graduated from UCSD with a bachelor of science in electrical and computer engineering and was simultaneously working on his company and pursuing a master’s degree with an emphasis on machine learning. He explains, “Something had to give.

He intends to resume his graduate studies in 2023 or 2024 and might use his own idea to complete his degree.

What are cloud computing jobs? Professionals with cloud computing expertise will be in great demand as more companies worldwide undertake significant digital changes. In 2018, professionals in this field earned a median of more than $146,000 — far above the median salary of $124,300 observed just two years previously. There were more than 50,000 cloud-based employment openings during the same time, with no indication of slowing down. We see the results in the post-pandemic period in 2022. Let’s take a closer look.

Are you looking for cloud computing jobs?

With the increasing popularity of technology, more individuals are turning to jobs in the computer industry. The future of cloud computing is extremely bright. This sector will see significant advancement as businesses embrace technology.

If you want to get into the field of technology, cloud computing is a wonderful place to start since it has so many job prospects. We’ll go through how to get a position in cloud computing in this comprehensive guide. We’ll discuss academic options, important abilities, career paths, and other key ideas. But first, what is cloud computing?

What is cloud computing?

The usage of cloud computing allows businesses to obtain technology services from a cloud provider on a pay-as-you-go basis rather than purchasing, housing, and managing physical data centers and servers.

Thanks to cloud storage services, users may save files and programs on remote servers and access them from anywhere via the Internet. Google Cloud Platform, Amazon Web Services, and Microsoft Azure are some of the most popular cloud computing providers.

If you’re curious about benefits of cloud computing, read our article.

Cloud computing explained

Many professionals are looking into cloud computing as a potential career move due to its increasing popularity. People seeking to break into the industry will benefit from having a technological background in general. A concentration in cloud computing with a technical bent can slam those doors shut.

Organizations transitioning to the cloud or launching new workloads are searching for skilled and knowledgeable professionals. Organizations with existing infrastructure and databases have to make the transition to the cloud. As a result, cloud computing job titles are more common and varied than ever before.

A career in cloud computing can be quite beneficial if you have IT expertise and technical skills or a desire to acquire them. Furthermore, it’s an industry that is always changing, making it practically future-proof.

There are many job titles in the cloud industry in today’s world. Some of these are rather specialized and innovative, and they can accommodate a wide range of talent sets. Recruits may work on network connections, servers, storage, analytics, software applications, and various other things. It’s simply a case of using one’s imagination: cloud expansion and development is not necessarily a one-time event.

According to Gartner, worldwide spending on cloud technology could top $150 billion by 2020. This is just to give you an idea of the potential for job creation in the industry. So, what do you need for cloud computing jobs?

Education for cloud computing jobs

Computer science, management information systems (MIS), and engineering are the most common degrees held by cloud computing experts.

Several colleges, such as Purdue University, now offer cloud computing degree programs that are gaining popularity.

Because several organizations are looking for particular cloud skillsets, you may distinguish yourself from other applicants if you improve your understanding of the following technical disciplines:

• Salesforce and customer relationship management (CRM) cloud development
• Programming languages (Python, Java, HTML, C/C++, SQL, NoSQL, and Linux are good places to start)
• Security best practices (consider widely recognized certifications like CISSP)
• Platform/brand-specific certifications and experience (Google Cloud, Microsoft Azure, AWS, etc.)
• DevOps and agile best practices

Required skills for cloud computing jobs

Numerous skill sets may get you a position working in the cloud. The more experience you have, the higher your chances of getting hired, but just like in certain IT fields, much of the learning might be obtained remotely. These skills and/or understanding can help you get ahead:

• Agile
• Amazon Web Services (AWS)
• Ansible
• Azure
• Chef
• Docker
• Java
• Puppet
• Python
• VMware

How to get a cloud computing job?

Follow these steps if you wish to work in the extremely lucrative cloud computing industry:

Learn about cloud computing and get certified with these courses: You may enroll in online training programs to improve and strengthen your cloud computing expertise. Get to know Microsoft Azure, Amazon AWS, and Google Cloud.

Understand the fundamentals of cloud computing: This is a stage that you should not overlook. A novice in cloud computing must understand the fundamentals of virtualization, storage, and networking. To get you started, take a look at this course for beginners.

Make contacts and friends: When looking for entry-level employment in this industry, make sure you contact employers directly since employees are always required in this field. Make connections on the inside so that you are recognized as soon as possible about possibilities, whether it’s a promotion later or gaining new abilities.

Experience hands-on: Getting experience in areas such as cloud computing architecture and design is crucial, whether it’s a paid job, a volunteer project, or a safe laboratory setting. Look for chances to work on projects that include system design and deployment for public/private/hybrid clouds.

Learn about the latest trends by getting acquainted with them: If you’re looking for a new career, knowing what’s trending in cloud computing can help you make an informed decision. This domain is changing rapidly; staying up to speed on new technologies and how they are used by different businesses is always beneficial.

Take the leap: Don’t be afraid to achieve your professional goals and objectives because of fear! If you believe in yourself and have what it takes to make this work, go for it! This is a field that will always be there, owing to how dramatically technology has altered our lives throughout the years. Cloud computing professionals are more essential than ever. As a result, if you’re ready, take that leap into being your own boss while also assisting businesses in growing their operations by utilizing sophisticated tools like infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and more.

You may specialize in a certain cloud platform’s services or acquire multi-platform expertise. You can concentrate on Amazon Web Services (AWS) or Microsoft Azure alone and become a cloud computing specialist in no time! Whatever you choose, these procedures will assist you in finding your ideal position within the field of cloud computing.

What are cloud computing jobs?

Many career paths exist in the cloud computing world, from administration to development-related opportunities. Here are some of the most popular cloud computing jobs today.

Back-end developer

You’ll be in charge of server-side web application logic and integrating front-end web developers’ work as a back-end web developer.

The back-end code that makes the applications work as intended will be written in programming languages. You’ll also be responsible for creating web services and APIs that back-end developers and mobile app builders will utilize in their jobs.

Cloud engineer

The primary responsibilities of a cloud engineer are to design, develop, and maintain cloud computing solutions for customers/organizations. This also means they must be able to communicate with both technical and non-technical team members, allowing them to get a first-hand understanding of business needs.

At this stage, you must be knowledgeable about various topics, including networking, storage systems, and virtualization. Cloud engineers should also be able to make the most of different sorts of infrastructure services provided by cloud computing businesses.

You must have practical experience and abilities in programming interfaces that may be utilized to automate activities, troubleshoot infrastructure resource issues, and improve the network device’s performance. To develop APIs that can readily connect with cloud computing solutions, you must be able to code in Python, Ruby, and other programming languages.

Data engineer

A data engineer is a software engineer who specializes in the installation and configuration of database systems and writing complex queries, and scaling to many machines. They also look for patterns in large data sets and develop algorithms to help organizations make the most of their raw facts.

Data scientist

Data scientists apply their skills across several IT disciplines. Still, they often concentrate on enhancing data quality and searchability and enabling cloud computing solutions involving machine learning (ML) and artificial intelligence (AI).

Development Operations Engineer (DevOps)

DevOps professionals are software engineers who oversee code releases and usually work with developers and IT staff. They used to be coders or system administrators interested in scripting and coding, but they’ve now evolved into deployers or network operators.

They’re now able to work on planning tests and deployment in DevOps, thanks to their expertise with the two.

Front-end developer

You’ll work on packaging the backend’s functionality as a front-end developer, which entails converting website design files into HTML, JavaScript (JS), or CSS code. Front-end developers use HTML, JavaScript (JS), or CSS to create the front end of a website.

Full-stack developer

Developers who work on the entire stack, such as database engineers and system designers, are known as full-stack developers. They deal with both the front and back ends of a website or application. During project planning, they assist clients in developing web stacks, mobile stacks, and native app layers.

Software engineer

A software engineer’s main responsibility is to develop and maintain the software that runs in a cloud infrastructure. This specialist also deals with malfunctions and faults in the program.

Systems administrator

DevOps Engineer is a technical position that works with software development, operations, and infrastructure. The Systems Administrators are in charge of operating and maintaining cloud computing systems, including networks and operating systems utilized in such environments to ensure that all applications function properly (24/365).

This role allows you to get started in the cloud computing industry. Because you’ll be working closely with clients/different teams from an organization daily, you’ll need strong troubleshooting talents, outstanding communication, and problem-solving skills.

Cloud developer

Cloud developers write and maintain applications that are deployed on public or private clouds. Although they do not generally work with the cloud infrastructure itself, they must be very knowledgeable about it to develop compatible apps.

Cloud security professional

The cloud security manager is in charge of the security of clouds. As a professional, you’ll need to know how to detect and respond if an assault occurs. You must be able to satisfy your customers’ security demands by comprehending the industry standards (with respect to cloud computing) as well as their regulatory requirements.

Cloud sales executive

This is a lucrative career that involves selling cloud computing services to consumers. You’ll need to comprehend your client’s business issues and offer them products/services that are efficiently tailored to their needs.

A Cloud Sales Executive must communicate effectively to illustrate how the solutions provided by various cloud vendors add value for businesses. This specialist should also be able to negotiate with vendors on behalf of their clients, ensuring quick and successful contract negotiations.

Cloud support engineer

In this position, you will be assisting customers with technical difficulties in cloud computing environments. You must be familiar with all sorts of clients, their needs, and how your firm’s products/services can assist them in resolving issues.

Data analysts

It’s a diverse position that requires extensive knowledge of information security and how different data is protected. To be a data analyst, you must understand SQL and use Business Intelligence (BI) tools to create visualizations, apply programming languages/statistical tools, and so on.

Digital skills officer

The digital skills officer is concerned with digital skill instruction, covering digital literacy, technology operations, and entrepreneurship. You’ll be in charge of developing course materials for workers who want to increase their current understanding or learn new things.

You’ll need to know how to communicate well and the ability to work alone and in groups. Knowledge of cloud computing platforms and cutting-edge technologies is also helpful.

Cloud consultant

Third-party cloud platforms and software providers’ consultants are known as cloud consultants. Although they may have an engineering or programming background, many cloud consultants previously worked in sales or marketing.

Where to find cloud computing jobs?

There are several sites where you may look for employment in cloud computing, including job boards, company websites, and cloud computing communities and forums. Let’s take a closer look at each of these alternatives.

Job boards

In today’s market, job boards are the most frequent way to discover available employment. You’re better off searching for opportunities on a technology-specific career platform like Cloudy Jobs, Dice, Hired, or AngelList. Nonetheless, don’t overlook more general sites like Indeed, LinkedIn, Glassdoor, and LinkUp. Stack Overflow and Women In Technology International (WITI) are two other options.

Company websites

Several firms include a current job offer on their career site. Look for organizations you want to work for and use their career websites to learn more about the people they need.

Industry communities and forums

Thousands of professionals from various occupations come together on different online sites to share their experiences, expertise, and knowledge. Cloud computing is no exception. Keep up with the latest industry news, developments, and open positions in cloud computing by reading Reddit, Spiceworks, Linux.org, and HardForum forums.

In 2022, these are some of the most sought-after cloud computing careers. If you have any extra options, please leave a remark below.

Hiring and getting hired in cloud computing

Since its inception, the cloud computing job market has been extremely robust, but with the expansion of remote employment and computing, more cloud computing companies and business customers are seeking for individuals with expertise in this field.

However, the supply of cloud prospects is restricted. Recruiters are now seeing a serious shortage of skilled IT professionals, which has resulted in cloud computing jobs being highly desirable for suitable people.

Consider requesting a larger budget when recruiting for cloud jobs if you’re a recruiter. Continue to expand on the skills and platform-specific knowledge that are in high demand right now and being rewarded if you’re a candidate in the cloud computing job market.

An automation engineer’s day-to-day tasks include designing and implementing technological processes that automate various activities. Automation technology is currently being used in various business, IT, and development processes, which has prompted organizations to seek these professionals to create, test, and implement automation technologies.

Automation is the use of data from various sources to streamline or enhance a system or process. By ensuring their operations are as efficient as possible while maintaining a high-quality output, automation engineering help businesses function more efficiently.

This is a field divided into two main branches. The first is run from a traditional engineering perspective and develops automated solutions for physical activities. On the other hand, modern version automates digital processes with software engineering.

What is an automation engineer?

An automation engineer is a skilled professional who uses technology to enhance, streamline, and automate processes. They are in charge of developing, putting into action, and monitoring such technologies. These engineers are employed in a variety of industries. Mechanical and computer automation are the two most prevalent varieties.

Is automation engineer a developer?

An automation engineer is an engineer that specializes in automating business operations. They use their skills to automate business processes carried out in various settings with software and robotics, resulting in increased productivity. Automation engineers generally need a degree in engineering. Most professionals enter the field via a mechanical, electrical, or software engineering curriculum. Technical knowledge may be acquired on the job for the most part.

What does an Automation Engineer do?

Automation engineers design, program, simulate and test automated machinery and processes. They’re generally found in industries like energy plants, automobile manufacturing facilities, food processing plants, and other environments utilizing robotics.

Automation engineers create detailed design specifications and automation based on precise needs for the process involved, adhere to worldwide and regional standards, process specific norms and rules.

Automation engineers may work in various settings and have a variety of duties. Depending on the environment, they may undertake tasks such as:

• Creating an automated work or manufacturing environment,
• Programming chatbots to answer inbound calls,
• Creating a system for IT support tickets processing and efficiently allocating them,
• Determining whether it’s necessary to automate specific steps in a process to minimize faults,
• Identifying and resolving problems in procedures with the least amount of downtime feasible,
• Installing and upgrading software, databases, and other solutions to improve efficiency.

Is automation a good career?

Automation engineering is a promising career for someone with the technical skills and desire to pursue a career in a technological field. Automation is a fast-paced industry in both technology and manufacturing. As technology advances, more and more activities are anticipated to be automated. As a result, the need for automation experts is likely to increase. Automated engineers command higher salaries than other IT workers, suggesting that the job is highly demanded.

Automation engineer salary (2022)

Automation engineers’ salaries in the US range from $40,000 to $228,000, with a median salary of $92,000. The middle 57% of these engineers make between $92,000 and $135,000 each year, with the top 86% earning an annual salary of $228,00.

What is industrial automation?

Industrial automation is integrating computerized machines, control systems, or other information technologies into business processes to perform work done by humans. Industrial automation uses both hardware and software to streamline mainly labor-intensive physical processes. It is widely used in smart factories and warehouses in production environments to facilitate production, assembly, and material handling.

What skills are required for IT automation?

Scripting, collaboration, source-code management, Kubernetes, security, testing, observability, monitoring, and network awareness are the minimal viable skills for IT automation.

Automation engineers require a wide range of technical and soft skills. They grasp the systems, networks, hardware, and software they are dealing with and can collaborate with other business units, clients, or customers. However, the languages and tools necessary for this position differ by sector.

Automation engineers need a comprehensive understanding of mobile, web, and desktop operating systems, alongside analytics, robotics, AI, and machine learning. Leadership abilities are also crucial since they must lead cross-departmental initiatives to simplify business procedures.

On 1 March 2021, EWOR kicked off off another round of its prestigious EWOR Fellowship program. 

“The concept,” its founder Daniel Dippold explains, “is uniquely designed to help corporations realize disruptive innovation and individuals to start ventures of global scale.” This March, an exciting mix of people, including former Miss India UK, who holds a Summa Cum Laude degree in law and a former Swiss math champion, join EWOR and a German real-estate fund with 3 BN assets under management to create outstanding ventures. We were interested in finding out what makes this concept so promising and how it attracted so many exceptional individuals.

EWOR was founded to empower individuals all over the globe to build outstanding ventures and innovations. Its fellowship is a blend of venture building, education, and networking, as shown below. Corporations are joined by a series of innovators with backgrounds in science, business, and computer science. EWOR is responsible for creating an ecosystem and educational approach in which all stakeholders thrive to build ventures of a global scale. Every year, an EWOR fellowship is awarded to approximately 10-30 individuals all over Europe. 

From our interview with its founder, we were amazed by two critical components of the program.

Machine Learning to Accelerate Talent Discovery

Firstly, machine learning played a role in this process. Daniel Dippold wrote a thesis titled ‘A Machine Learning Analysis of the Impact on Cognitive Dimensions on Success in Entrepreneurship,’ part of which was an algorithm that filtered non-successful entrepreneurs correctly in 97.1% of all cases. The thesis was awarded a distinction from Cambridge University. The founder explains that he could use a variety of factors that machine learning can turn into an ‘entrepreneurial potential map.’ 

He explains further that, opposed to standard IQ tests, many of the factors identified by EWOR’s algorithm are indeed malleable. It helps identify where an individual needs to develop and are by no means a fixed number, such as IQ, that cannot be altered over time. EWOR has built an entire concept around this notion of malleable entrepreneurial potential, incorporated in its platform ewor.io. 

“The gist is,” Daniel explains, “that school teaches us only one important dimension of intelligence and neglects others that are essential for starting a successful business.” At the University of Cambridge, Daniel researched what are called “environments of unknown unknowns.” Such environments are so complex that it is impossible to factor in every component necessary for making a good decision. There are not only factors one knows are unknown but also factors that one isn’t even aware of. In such environments, things work differently. That is why EWOR has developed a new educational concept that discards concepts essential to ordinary education, such as having a curriculum. 

A Win-Win-Win Situation

Secondly, the EWOR concept is designed to create a win-win-win situation for EWOR fellows, EWOR corporations, and EWOR itself. Partnership companies, who offer their expertise, office space, and other resources to EWOR fellows, benefit from being exposed to innovations it could not possibly create in-house. 

Having conceptualized both incubator and accelerator programs before, Daniel is convinced that both concepts are not ideal for corporate innovation. Instead, an approach needs to be chosen that allows corporations to innovate around their core business but at the same time avoid paralyzing the entrepreneurial spirit of founders. This is a common problem with incubators, where corporations exercise too much control – or are forced to do so because of compliance reasons – which ultimately stymies entrepreneurs. 

An Outstanding Network of Advisors

A look at the advisory board of the company supports our confidence in the program. Personalities such as Alexander Grots, the former Managing Director of IDEO Europe, the world’s largest innovation agency that invented the world-known term ‘design thinking,’ helped co-design much of EWOR’s education platform. Chris Coleridge, Professor at University of Cambridge, founder of multiple accelerator programs and founder of Europe’s first Master’s degree in entrepreneurship, helped design EWOR’s learning map. 

Individuals such as Daniel Marasch, former Executive Board at Lidl, and Alex Schwörer, Owner and former Executive Board of 6000-employee firm PERI, represent large corporations’ voices in EWOR’s programs. Finally, serial inventor and unicorn founder Mattias Bergstrom co-developed and challenged many of their assumptions around building global-scale ventures. 

The Partnership Company

The partnership company, Project Gruppe, is a Germany-based real estate fund and developer with over 25 years of track record within the industry. Project manages over 3 BN €s of assets and owns the entire value chain from raising capital to developing and selling properties. The exclusive partnership between the fund manager and real estate developer allows Project Gruppe to operate efficiently and thus deliver a high financial return to its investors. Christian Grall, CEO of Project Vermittlungs GmbH, states that ‘he is excited to see the EWOR fellows challenge their status quo.’

The Fellows

Lastly, we were impressed by the talent EWOR was able to attract with its program. This year’s fellowship awards the following candidates with an EWOR fellowship:

Suhani Gandhi

Suhani Gandhi holds a Summa Cum Laude Bachelor’s degree in Law and is a scholarship awardee at Imperial College Business School, where she is currently pursuing a Master’s Degree in Strategic Marketing. Suhani founded the first-ever online Hindi school, Holiday Hindi, facilitating language learning through Indian arts and culture. She is an award-winning actress who has appeared in projects on Netflix and Amazon Prime. Suhani was titled Miss India UK in 2014 and nominated by Times Now as NRI of the Year for her contribution to the arts sector. 

Yannick Müller

Yannick Müller is pursuing a Bachelor’s degree in Computer Sciences at ETH Zurich. He participated in over 10 Hackathons, of which he won some. Yannick developed AI solutions to classify web attacks and conceptualized a solar house with a rooftop that allows solar beams to enter during Winter. He is a Swiss math champion and is currently preparing for an Ironman. 

Ayoub Boukir

Ayoub Boukir has a Summa Cum Laude bachelor’s degree in Business Administration and is enrolled in a Master’s Degree in Finance and Accounting at the University of St. Gallen. Ayoub founded a project which used drones to map agricultural land. He is the VP of Finance and Fundraising at World Merit Morocco and has consulted companies and funds investing in Africa.

Clarissa Heidenreich

Clarissa Heidenreich built up Afya Nutrition, a social startup that tackles malnutrition in African countries with the microalgae Spirulina. She has a degree in Business Law and is pursuing a second degree in Corporate Law. Clarissa received multiple recognitions, such as being picked as a McKinsey Firsthand, an EY Future Female Talent, and more. In 2018, she won the National Championship with Enactus Mannheim. Clarissa received several scholarships and awards for academic excellence and has gathered practical business expertise as CEO of Begapinol Dr. Schmidt GmbH. 

William McSweeney

William McSweeney holds degrees in History and Human Rights. In his work at a LawTech company, he has automated a data protection e-learning solution, which netted £200k within 18 months. William led a research project and developed a solution to reduce barriers to access digitized legal advice. His work focuses on bridging the gap between technology and law and increasing access to justice through innovation.

Gatsby Fitzgerald

Gatsby Fitzgerald holds a Bachelor’s degree in Medicinal Chemistry at Imperial College London and is now enrolled in a Joint Honours Management program at the Imperial College Business School. Gatsby has developed a technology to recycle Li-ion batteries during a 12 weeks competition and frequently competes in Marathons and Ironmans.

Sebastian Rappen

Sebastian Rappen focused on Cultural Studies of the Middle East and Philosophy at Eberhard Karls University Tübingen, studied design thinking at Stanford in cooperation with the SUGAR network, and graduated with an interdisciplinary Master in Organisational Design from the University of St.Gallen. Sebastian is the former Content Lead Design Thinking for EY and is a Lecturer for Digital Company Culture at BVS St.Gallen. He is a consultant for human-centered design in the health care and finance sector.

Jivan Navani

Jivan Navani is currently pursuing a Master’s in Entrepreneurship at the University of Cambridge and holds a Bachelor’s in Management from the London School of Economics. At the LSE, he served on the court of governors and was the President of the Investment Society. Jivan has founded numerous successful businesses and has been part of the 2019 cohort of the Y Combinator Startup School. Additionally, he is the former Head of Operations at London Blockchain Labs and former Head of Venture Investment at European Student Startups.

Melanie Preen

Melanie Preen is a fresh student at The University of Manchester, where she is enrolled in the Information Technology Management’s honors program. She co-founded a 3D printing prosthetics club and was recognized for the International School Awards 2021, where she built a prosthetic hand for a girl born with amniotic band syndrome. She is the demo day innovation winner for the LaunchX Entrepreneurship Program in South East Asia, where her team ideated a gamified, remote-controlled biomimicry fish that consumed plastic. She has fundraised successfully for her Cambodian School via VR and has curated and co-hosted the first public TEDx event in Phuket to give a voice to the community.

Aldiyar Semedyarov

Aldiyar Semedyarov is pursuing a Master’s degree in Electrical Engineering and Information Technologies from ETH Zurich. He co-founded the startup Qoqys to address the waste management challenges in Kazakhstan. He won the ABC Incubation program by Nazarbayev University Research and Innovation System and the Fostering Research and Innovation Potential program by Nazarbayev University Young Researchers Alliance. Aldiyar has received a certificate of appreciation from the mayor of Aktau city for the city’s socio-economic development and is a gold and silver medal winner of Kazakhstan’s national physics olympiads. He co-authored two publications that were presented at esteemed international scientific conferences.

When we think of data science, we rarely think beyond those people with the technical ability, knowledge, training, and qualifications necessary for the job.

But decision-makers need to be involved in data science too.

Whether you need to understand data science, know how to approach solution providers, or be better positioned to hire data scientists, having a solid foundation in data science and business strategy can be crucial to your organization.

The Tesseract Academy helps educate decision-makers on topics like what data science and AI are, how to think like a data scientist without being one, the fundamentals of hiring and managing data scientists, and building a data-centric culture. The Tesseract Academy runs a free event called the Data Science and AI clinic, to help decision-makers better understand how they can utilize data science in their companies.

“The attendees of our programs immerse themselves into the workshop and then come out of it with a clear, actionable plan,” Dr. Stylianos Kampakis, CEO and instructor at The Tesseract Academy, told me. “So, our workshops are crash courses for any non-technical professional who is thinking to use data science and doesn’t understand how. The most important part is the interactive exercises, which help drive the data strategy plan.”

Dr. Kampakis has been in data science and AI for many years and has worked with companies of all sizes, from solopreneurs to big corporates such as Vodafone. He is also a data science advisor for London Business School and works with various universities, including UCL and Cambridge University’s Judge Business School. He is also a published author.

For CEOs, founders, managers, entrepreneurs, and product managers, taking a data science workshop from a strategic and business perspective could give their businesses a competitive edge. After all, 2021 is looking to be a pivotal year in staying ahead of the game with data science.

“I know that executives are busy people,” Kampakis said. “That’s why I wanted to create something which can give them results as fast as possible. It’s a win-win because even I’ve seen people and companies grow due to my teachings, and they will always come back to me for further coaching later down the line. There is nothing more rewarding than seeing a client get ahead of the competition, as a result of the methods and tools I teach.”
Anyone interested can visit The Tesseract Academy’s website for further details and register for the event.

Data science is one of the hottest jobs in IT and one of the best paid too. And while it is essential to have the right academic background, it can also be crucial to back those up with the proper certifications.

Certifications are a great way to give you an edge as a data scientist; they provide you with validation, helping you get hired above others with similar qualifications and experience.

Data science certifications come in many forms. From universities to specific vendors, any of the following are recognized by the industry and will help you hone your skills while demonstrating that you fully understand this area of expertise and have a great work ethic.

Certified Analytics Professional

The Certified Analytics Professional (CAP) is a vendor-neutral certification. You need to meet specific criteria before you can take the CAP or the associate level aCAP exams. To qualify for the CAP certification, you’ll need three years of related experience if you have a master’s in a related field, five years of related experience if you hold a bachelor’s in a related field, and seven years of experience if you have any degree unrelated to analytics. To qualify for the aCAP exam, you will need a master’s degree and less than three years of related data or analytics experience.

The CAP certification program is sponsored by INFORMS and was created by teams of subject matter experts from practice, academia, and government.

The base price is $495 for an INFORMS member and $695 for non-members. You need to renew it every three years through professional development units.

Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate (CCA) Data Analyst certification shows your ability as a SQL developer to pull and generate reports in Cloudera’s CDH environment using Impala and Hive. In a two-hour exam, you have to solve several customer problems and show your ability to analyze each scenario and “implement a technical solution with a high degree of precision.”

It costs $295 and is valid for two years.

Cloudera Certified Professional Data Engineer

Cloudera also provides a Certified Professional (CCP) Data Engineer certification. According to Cloudera, those looking to earn their CCP Data Engineer certification should have in-depth experience in data engineering and a “high-level of mastery” of common data science skills. The exam lasts four hours, and like its other certification, you’ll need to earn 70 percent or higher to pass.

The cost is $400 per attempt, and it is valid for three years.

DAMA International CDMP

The DAMA International CDMP certification is a program that allows data management professionals to enhance their personal and career goals.

The exam covers 14 topics and 11 knowledge areas, including big data, data management processes, and data ethics. DAMA also offers specialist exams, such as data modeling and design, and data governance.

Data Science Council of America Senior Data Scientist

The Data Science Council of America Senior Data Scientist certification program is for those with five or more years of research and analytics experience. There are five tracks, each with different focuses and requirements, and you’ll need a bachelor’s degree as a minimum. Some tracks require a master’s degree.

The cost is $650, and it expires after five years.

Data Science Council of America Principal Data Scientist

The Data Science Council of America also offers the Principal Data Scientist certification for data scientists with ten or more years of big data experience. The exam is designed for “seasoned and high-achiever Data Science thought and practice leaders.”

Costs range from $300 to $950, depending on which track you choose. Unlike the other certifications so far, this does not expire.

Google Professional Data Engineer Certification

The Google Professional Data Engineer certification is for those with basic knowledge of the Google Cloud Platform (GCP) and at least one year of experience designing and managing solutions using GCP. You are recommended to have at least three years of industry experience.

It costs $200, and the credentials don’t expire.

IBM Data Science Professional Certificate

The IBM Data Science Professional certificate comprises nine courses, covering everything from data science to open-source tools, Python to SQL, and more. In an online course, you’ll create a portfolio of projects as part of the certification, which is useful for employers who need to see practical examples of your work.

There is no charge for this course and no expiry.

Microsoft Azure AI Fundamentals

Microsoft’s Azure AI Fundamentals certification focuses on machine learning and AI but specific to Microsoft Azure services. A foundational course, it is suitable for those new to the field.

It costs $99 with no credentials expiry.

Microsoft Azure Data Scientist Associate

Microsoft also provides the Azure Data Scientist Associate certification focused on machine learning workloads on Azure. You’ll be tested on ML, AI, NLP, computer vision, and predictive analytics, and it requires more advanced knowledge of the field than its other certification program.

The cost is $165, and again, credentials don’t expire.

Open Group Certified Data Scientist

The Open Group Certified Data Scientist (Open CDS) certification is markedly different from the other programs listed here. There are no traditional training courses or exams. Instead, you gain levels of certification based on your experience and a board approval process.

The cost depends on which level you are applying for, but the minimum fee is $1,100 to reach level one. Credentials don’t expire.

TensorFlow Developer Certificate

The TensorFlow Developer Certificate is for those who want to show their machine learning skills using TensorFlow. You will need experience with ML and deep learning’s basic principles, building ML models, image recognition, NLP, and deep neural networks.

This certification costs $100 per exam, and credentials don’t expire.

Smart classrooms aren’t too far off, and blockchain technology may become an integral part of schools all over the globe in a few years. But how will this system help administrators and students?

Bitcoin is the most well-known virtual currency in the world, at one time reaching a value of over $19,000 per coin. Its biggest legacy, however, has nothing to do with its value, but with the type of network it uses.

The blockchain, which has gained fame over the last few years for its superior cybersecurity capabilities, has begun to see use in a number of industries that take security seriously, including finance and healthcare. The potential uses for the blockchain, however, extend far beyond its current applications and could make a big difference in classrooms one day.

Education is a sector that is just as important as healthcare and finance, and there are lots of areas within this sector that could be improved using technology. The market for edtech is growing quickly, and is estimated to reach $93.76 billion globally by 2020. Already, tools like virtual reality and personalized learning with artificial intelligence are helping to improve learning outcomes for students at all levels.

Smart classrooms aren’t too far off, and blockchain technology may become an integral part of schools all over the globe in a few years. But how will this system help administrators and students? To understand the potential impact of blockchain in the education system and predict how this technology will impact teachers and students, it’s helpful to know how other sectors have used it to improve their processes—and how schools might one day follow in their footsteps.

Banks and Hospitals Paving the Way for Better School Security

Globally, people are understandably worried about their privacy and the security of their data. Parents are especially protective of their children’s data, and schools have to take the threat of data breaches seriously as online records become more common every year.

Schools can follow the lead of industries like finance and healthcare and use the blockchain to help keep student data safe in the coming years. These industries have been paving the way for years and the infrastructure that will allow schools to secure their data with blockchain technology is finally being built, thanks to the successful tests and experiments conducted by healthcare and financial organizations.  

Banks and financial institutions are understandably security-focused, and have been pioneers in using blockchain technology. Although the process of updating the blockchain’s distributed ledger can be slow, it is much faster than many other digital means of transferring value.

In healthcare, blockchains are being used to help reduce the impact of cybercrime and data theft in the industry. An astounding 1 out of every 4 breaches targets the healthcare sector, affecting 1 in 13 patients in the United States. Healthcare data is extremely valuable to hackers because it is so personal, permanent, and detailed. To help fight off cybercriminals, many healthcare organizations and insurers are either using or considering using blockchain technology. The transparency of the system, combined with the fact that nothing is truly deleted, makes it much more difficult to steal patient data undetected.

Security Concerns and Degree Verification on College Campuses

Unfortunately, education isn’t far behind finance and healthcare when it comes to data breaches. After these two industries, education experienced the most data breaches of any sector in 2017, making up 13% of all breaches. Student data, although it might seem worthless since most young children and teens have limited financial information, is actually becoming a hot commodity among cybercriminals.

Security and verification are becoming a major concern both on college campuses and after students leave to enter the workforce. Data breaches target student records and steal information that can be used to create fake identities or be sold by hackers. Protecting records with the blockchain could make these attacks ineffective, protecting students’ identities and school records. As more schools from kindergarten to university go digital, this could be key in ensuring student privacy.

Employing blockchain security protocols in higher education has its other uses as well—namely defending employers against people who claim to have a degree, but really don’t. Unfortunately, people have been known to lie about their degrees and qualifications to employers, claims that are difficult (if not impossible) to verify under current systems. When students enter the workforce, blockchain could also be used to help assure employers that potential candidates fresh out of school have the qualifications that they claim on their resume, by storing that information in a secure ledger.

Potential Uses for Blockchain Technology in the Education Sector

By looking to other industries that currently use blockchain technology, we can start to make predictions about how these networks could be used in an educational setting. While the number of potential uses in education is very large, there are a few that are the most exciting.

Test Prep and Learning

Russian platform Disciplina is the first platform to harness the power of blockchain technology solely for education and recruiting. TeachMePlease, one of the Disciplina applications, is a higher education marketplace, bringing teachers and students together.

Another blockchain platform that’s emerging in this space is Opet Foundation’s chatbot app to help students with test prep. It answers questions and recommends resources while keeping track of student progress in the blockchain.

Library and Information Services

Because it is easier to keep track of and store information, the blockchain could be used to enhance library and information services in schools. Though few libraries have started experimenting with blockchain technology, at least one school has received a substantial grant to begin the discovery process.

Transportation

Getting students to school is an important part of educating them. In the future, ridesharing apps resting on blockchain technology could be used to organize carpools for students with special needs. This will be particularly important as roadways become more congested and carpooling becomes a necessity. Using the blockchain in this way would also take the burden off of parents and ensure that all children get the safe transportation they need.

Looking Toward the Future

Because blockchain use in the education space is still very new and unexplored, there are a lot of unanswered questions. What is clear, however, that the possibilities are endless. As school administrators look to the future, they would do well to think about how to harness this powerful technology.

The world is increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is only going to continue growing in the coming years. It is a fantastic career move and it could be just the type of career you have been trying to find.

Professionals who are working in this field can expect an impressive salary, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of the data scientists say there is currently a shortage of professionals working in the field.

What Type of Education Is Needed?

Most data scientists – 92% – have an advanced degree. Only eight percent have a bachelor’s degree; 44% have a master’s degree and 48% have a Ph.D. Therefore, it stands to reason that those who want to boost their career and have the best chance for a long and fruitful career with great compensation will work toward getting higher education.

Some of the most common certifications for those in the field include Certified Analytics Professional (CAP), EMC: Data Science Associate (EMCDSA), SAS Certified Predictive Modeler and Cloudera Certified Professional: Data Scientist (CCP-DS). The various certifications are for specific competencies in the field.

Now is a good time to enter the field, as many of the scientists working have only been doing so for less than four years. This is simply because the field is so new. Getting into big data and analytics now is getting in on the ground floor of a vibrant and growing area of technology.

Multiple Job Roles

Many who are working in the field today have more than one role in their job. They may act as researchers, who mine company data for information. They may also be involved with business management. Around 40% work in this capacity. Others work in creative and development roles. Being versatile and being able to take on various roles can make a person more valuable to the team.

Being willing to work in a variety of fields can help, too. While the technology field accounts for 41% of the jobs in data science currently, it is important to other areas too. This includes marketing, corporate, consulting, healthcare, financial services, government, and gaming.

Add More Skills

To become more attractive to companies, those who are in the big data and analytics fields can work to add more skills by taking additional courses. Some of the options to consider include:

⦁ Hadoop and MapReduce

⦁ Real Time Processing

⦁ NoSQL Databases

⦁ GTA Support

⦁ Excel

⦁ Data Science with R

⦁ Data Science with SAS

⦁ Data Science with Python

⦁ Data Visualization – Tableau

⦁ Machine Learning

⦁ Cloudlabs for R and Python

Continuing to take classes will provide you with the edge needed to become a valuable member to any team. It shows initiative and drive, and it makes you more of an asset to companies.

Keep Up With the Changes

The field of big data and analytics is not static. As technology changes and increases, so will the field. It is vital that those who are in the field and who want to remain in the field take the initiative to stay up to date with any changes that could affect the field.

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This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive.

First intro: Hacker News and Udacity

My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn’t even a professional developer, but a hobby coder who’d done a couple of small projects.

So I began watching the first few chapters of Udacity’s Supervised Learning course, while also reading all articles I came across on the subject.

This gave me a little bit of conceptual understanding, though no practical skills. I also didn’t finish it, as I rarely do with MOOC’s.

Failing Coursera’s ML Course

In January 2015 I joined the Founders and Coders (FAC) bootcamp in London in order to become a developer. A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. Every Tuesday evening, we’d watch lectures from Coursera’s Machine Learning course.

It’s a fantastic course, and I learned a hell of a lot. But it’s tough for a beginner. I had to watch the lectures over and over again before grasping the concepts. The Octave coding task are challenging as well, especially if you don’t know Octave. As a result of the difficulty, one by one fell off the study group as the weeks passed. Eventually, I fell off it myself as well.

I hindsight, I should have started with a course that either used ml libraries for the coding tasks — as opposed to building the algorithms from scratch — or at least used a programming language I knew.

Learning a new language while also trying to code ml algorithms is too hard for a newbie.

If I could go back in time, I’d choose Udacity’s Intro to Machine Learning, as it’s easier and uses Python and Scikit Learn. This way, we would have gotten our hands dirty as soon as possible, gained confidence, and had more fun.

Lesson learned: Start with something easy and practical rather than difficult and theoretical.

Machine Learning in a Week

One of the last things I did at FAC was the ml week stunt. My goal was to be able to apply machine learning to actual problems at the end of the week, which I managed to do.

Throughout the week I did the following:

• got to know Scikit Learn
• tried ml on a real world dataset
• coded a linear regression algorithm from scratch (in Python)
• did a tiny bit of nlp

It’s by far the steepest ml learning curve I’ve ever experienced. Go ahead andread the article if you want a more detailed overview.

Lesson learned: Setting off a week solely to immerse yourself into a new subject is extremely effective.

Failing neural networks

After I finished FAC in London and moved back to Norway, I tried to repeat the success from the ml week, but for neural networks instead.

This failed.

There were simply too many distractions to spend 10 hours of coding and learning every day. I had underestimated how important it was to be surrounded by peers at FAC.

Lesson learned: Find an thriving environment to surround yourself with when doing these kinds of learning stunts.

However, I got started with neural nets at least, and slowly started to grasp the concept. By July I managed to code my first net. It’s probably the crappiest implementation ever created, and I actually find it embarrassing to show off. But it did the trick; I proved to myself that I understood concepts likebackpropagation and gradient descent.

In the second half of the year, my progression slowed down, as I started a new job. The most important takeaway from this period was the leap from non-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university.

By the end of the year I wrote an article as a summary of how I learned this.

Testing out Kaggle Contests

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests.

The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data.

I learned to trust my logic when doing machine learning.

If tweaking a parameter or engineering a new feature seems like a good idea logically, it’s quite likely that it actually will help.

Setting up a learning routine at work

Back at work in January 2016 I wanted to continue in the flow I’d gotten into during Christmas. So I asked my manager if I could spend some time learning stuff during my work hours as well, which he happily approved.

Having gotten a basic understanding of neural networks at this point, I wanted to move on to deep learning.

Udacity’s Deep Learning

My first attempt was Udacity’s Deep Learning course, which ended up as a big disappointment. The contents of the video lectures are good, but they are too short and shallow to me.

And the IPython Notebook assignments ended up being too frustrating, as I spent most of my time debugging code errors, which is the most effective way to kill motivation. So after doing that for a couple of sessions at work, I simply gave up.

To their defense, I’m a total noob when it comes to IPython Notebooks, so it might not be as bad for you as it was for me. So it might be that I simply wasn’t ready for the course.

Stanford — Deep Learning for NLP

Luckily, I then discovered Stanford’s CS224D and decided to give it a shot. It is a fantastic course. And though it’s difficult, I never end up debugging when doing the problem sets.

Secondly, they actually give you the solution code as well, which I often look at when I’m stuck, so that I can work my way backwards to understand the steps needed to reach a solution.

Though I’ve haven’t finished it yet, it has significantly boosted my knowledge in nlp and neural networks so far.

However it’s been tough. Really tough. At one point, I realized I needed help from someone better than me, so I came in touch with a Ph.D student who was willing to help me out for 40 USD per hour, both with the problem sets as well as the overall understanding. This has been critical for me in order to move on, as he has uncovered a lot of black holes in my knowledge.

Lesson learned: It’s possible to get a good machine learning teacher for around 50 USD per hour. If you can afford it, it’s definitely worth it.

In addition to this, Xeneta also hired a data scientist recently. He’s got a masters degree in math, so I often ask him for help when I’m stuck with various linear algebra an calculus tasks, or ml in general. So be sure to check out which resources you have internally in your company as well.

Boosting Sales at Xeneta

After doing all this, I finally felt ready to do a ml project at work. It basically involved training an algorithm to qualify sales leads by reading company descriptions, and has actually proven to be a big time saver for the sales guys using the tool.

Check out out article I wrote about it below or head over to GitHub to dive straight into the code.

Getting to this point has surely been a long journey. But also a fast one; when I started my machine learning in a week project, I certainly didn’t have any hopes of actually using it professionally within a year.

But it’s 100 percent possible. And if I can do it, so can anybody else.

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Due to the advances of modern technology, there has been a great change in the academic landscape. This substantial change has already had a great impact on the overall performance of students due to the more sophisticated approach to learning in general.

Historically speaking, traditional teaching methods had a great deal of disconnect with the way that students learn, which is why so many of them fell behind. Given that the current generation is very digitally oriented, the educational sector has to switch its methods in order to be able to relate to these students who have grown up in the digital age. In this article, we bring you the story of big data and its impact on student performance and learning approaches.

A Change in Perspective

Given the age we live in, it comes as no surprise that various forms of online learning are becoming prevalent in comparison to traditional offline methods. For example, many of the traditional learning mediums are moving entirely online – it is enough to think about physical books and e-books, paper notebooks and tablets, standard classrooms and online courses. In fact, according to a recent study by the Babson Survey Research Group in 2015, it is likely that every student will be enrolled in at least one distance learning course during their academic careers in the coming years.

For example, in 2015, the percentage of students enrolled in at least one distance course increased 3.9% from the prior year. Due to this transition, there has been a plethora of data generated with the purpose of understanding how students learn. Since the data has been gathered, technological experts have been working in conjunction with educational institutions in order to transform this data into ways to increase the potential for students to be able to learn more in the classroom. 

Insights into Students’ Preferred Learning Modes

Today, educational facilities increasingly utilize tech-based educational solutions, including various online learning platforms. Many of those platforms have a feature that allows them to track information about the learning process of each student. When that information is combined with the general knowledge about students’ behavioral patterns, it is possible to gain valuable insights into ways that students learn.

For example, there is a tool called Eduvant which can record information on the ways that students learn. This platform generates data ranging from student performance to warnings about certain student´s slow progress. With its daily information and actionable insights, this is one of the many great tools out there that provides useful findings about students.

By analyzing those findings, educational institutions can decide on which learning approach is the best for each of their students. This will also allow the institution to pick up on different learning styles that tend to vary in correspondence with the subject that is being taught. A viable example of this can be seen when a specific student excels in a self-paced learning technique in a history class, but needs an instructor-led course when studying chemistry.

Personalized Learning Experiences

In the past, educators had to monitor dozens (even hundreds) of students in order to ascertain which of them had difficulties. However, it was often really hard to identify those students that needed additional attention. For example, a low score on an examination was the only way that a teacher could assess the student’s lack of understanding. Today, there´s the data-based approach to help both students and teachers.

The data-based approach periodically tracks an individual student’s performance by using indicators such as: prior knowledge, level of academic ability, and individual interests. What this approach achieves is that it allows for personalized learning where the students can actively learn at their own pace. Furthermore, educators can provide their support, tools, and assistance to those students who need their attention in the classroom.

One of the highly regarded platforms that features personalized courses and exercises is Khan Academy. Aside from the fact that it can be used by students and parents, this platform also allows teachers to provide individualized video tutorials and practices in many different subjects, predominately math. Specifically, teachers can modify tutorials and playlists and recommend certain videos and exercises to students. Additionally, students have the ability to set their own goals, and teachers have access to all the data.

Khan Academy is only one of the many similar platforms that are used nowadays – each teacher can find what fits them best.

Immediate Feedback

Another advantage of big data is that it enables immediate feedback and real-time updates that make the teaching/learning approach substantially enhanced. For example, as the instructors receive information about the students, they are able to provide immediate responses through mediums such us online forums or chats. Many of the online learning platforms used by educational institutions utilize continuous feedback, which allows students to be more engaged in the learning process.

Consider, for instance, a simple tool such as Canvas. By structure, it is a learning platform that enables teachers to use audio and video mediums to provide students with instructions and feedback about their work. There is also Google Classroom app, for example. It is designed predominantly for teachers and tutors who can use it to make lesson plans and assignments, and communicate with students.

The timely communication of such tools allows instructors to correct the errors of their students faster than ever before. On the other hand, students can also contribute with they own feedback. For example, they can ask for a clarification of certain content or troubleshoot technical difficulties. Big data makes everything run faster and more smoothly.

Improvements to Learning Materials 

The data-based approach allows for constant monitoring of how students interact not only with their colleagues and educators but also with the learning materials. By analyzing the learner data for a large number of students and courses, educators are able to see which educational strategies and exercises are effective and which are not.

An example of this can be seen when learners skip some learning materials (e.g. videos) and still have good results. Such patterns provide guidelines for teachers on what to do next – in this specific scenario, they can omit videos and focus on other teaching methods. Lastly, big data also enables educators to see which strategies work for a specific target group of students. Oftentimes, there will be no universal solution.

Final Thoughts

Big data has the potential to make a great impact on the classroom. Not only does it provide valuable information about student’s preferred learning modes, but it also enables personalized learning experiences. This might be most helpful for those students that are struggling with certain learning issues.

Moreover, big data enables educators and students to interact quickly and efficiently, and it also comes in handy when there´s a need to improve learning materials. It will be incredible to see how big data progresses in the coming years and how it improves the success of students around the globe.

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Image: Alan Cleaver

What do you want to be one you grow up? A data scientist, of course! Though ultra popular, the modern field of data science is relatively new. It’s still developing, which makes it incredibly hard for youngsters to get into it early. Kids can take coding courses to see if they want to work with computers, or writing courses to see if they are burgeoning journalists, but data science is more confusing. Data science requires no single skill set, and most degree programs are very new and sometimes even of questionable value. So how can excited students get into the field of data science?

In order to get into data science, it’s paramount to first understand its history. While “big data” is a modern day buzzword, data science isn’t entirely new or revolutionary. Students of data science aren’t taught strictly “data science” skills, rather they must become skilled in a variety of disciplines. Early data scientists didn’t have degrees in Big Data. They were computer scientists and statisticians who filled a new gap created by emerging technologies and possibilities. Data scientists must not only be knowledgable in code, they must be able to understand complex algorithms and love solving problems.

Learn Data Science Skills On Your Own

Going into data science means acquiring several useful skill sets that can be employed across several sectors, from pharmaceuticals and research to marketing and technology. But it also requires a lot of commitment. Luckily, there’s plenty of online courses to start the fire. Try testing out an Introduction to Data Science course on an online education platform like Coursera or Udemy. It’s hard not to get excited about data science after seeing all the possibilities! Follow that up by trying your hand at programming, either in R or Python. In school, be sure to take statistics, perhaps even an advanced class. If all these topics still interest you, you may be looking at a career in data science. You can also try courses in linear algebra or machine learning to further test the waters.

Prepare For a Degree Program

Will you need a degree to become a data scientist? Probably. In fact, 88% of data scientists have a master’s and 46% have a PhD. Most of these scientists, however, never took specialized “data science” courses. Many of them started in related fields and then turned their skills toward data science. The real question is what should you study to become a data scientist?

The answer is heavily dependent on the individual. More surprisingly, employers and working data scientists hold a lot of skepticism over specialized “data science” degrees. Not every degree program is worthwhile, and many programs are simply repackaged existing courses with no deeper understanding of data science. Some insiders recommend getting a BA in statistics to create a solid theoretical foundation for your career. Many more suggest supplementing traditional statistical and computer science studies with online courses in data science topics like SQL, NoSQL or Hadoop.

When choosing a university program, it’s key to choose based on the quality of the curriculum and professors rather than just the title. A degree in data science is useless if it doesn’t include the skills required for the job. When choosing a bachelor’s program, be sure it will enable you to pursue a master’s. Even if it seems far off or impossible, a degree in computer science, mathematics, statistics or engineering may be paramount to getting into the field more easily.

Perfect the Soft Skills

The term “soft skills” refers to abilities that are personal rather than learned skills like coding. In data science, soft skills are actually much more important than they appear. This has a lot to do with the career trajectory of data scientists. A degree or certain skill isn’t necessarily a “fast track” into real data science work. On top of powerful skill sets, data scientists must be adaptable and prepared to use their abilities in a variety of ways. It’s important to prove you have not only theoretical knowledge, but practical. Building a portfolio is just as important as going to class. More importantly, students can build portfolios completely on their own.

After grasping the basics of data science and analytics, students can play around with data tools and create real results. There are several open source tools available to mine data, to analyze it or create visualizations. Try asking a question and use data to find the answer. Data can be found all over the internet, often in nice downloadable collections. Try mining Twitter for information on what’s popular or who’s saying what. Learn from Wikidata and put your findings into visualizations. Open source programs and open data are all free to use. Technology will always be changing, but it’s good to become acquainted with popular programs and how they work. Degrees may teach skills, but doing data science is the only way to get good at data science.

Get Comfortable With Data and Have Fun!

While data science is full of theoretical skills that can be tough and time-consuming to learn, don’t forget about the fun aspects of data. The internet is full of great datasets and visualizations, so get inspired by what’s out there! Check out TedTalks on data usages to see how people are using data in real life. Read up on the history of data science to understand where it comes from and what it means. Try to understand all the different ways data science is used.

If you’re getting stuck on the lingo, try the Big Data Dictionary. Or read up on the three most important algorithms and find out what they really do. Tune into a data podcast on your drive to school. There’s no one path to becoming a data scientist, so find out what part of data excites you, follow it, and make your way into data science.
image credit: Francisco Osorio

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If you didn’t already know, let me be the first to break the good news to you: data on the Indian data analytics sector is very encouraging. Today, India’s BI (Business Intelligence) and Data Analytics industry is worth a princely $10 billion and is expected to skyrocket to $26.9 billion by 2017.

These numbers aren’t surprising. India’s dominance as an IT services hub, in conjunction with its small but highly skilled white collar workforce—one that is well-trained in computational technologies and quantitative research methodologies such as mathematics and statistics–has allowed it to reap the benefits of the data revolution.

And according to an article written in Times of India Rituparna Chakraborty, co-founder & senior VP of TeamLease Services is quoted saying:

“India will face a demand-supply gap of 2,00,000 analytics professionals over the next three years. Even in the US, only 40 out of 100 positions for analytics professionals can be filled.” Today, India has emerged as a key player in the global data analytics sector, particularly in the domain of B2C service provision. The speedy transition from IT to next-generation technologies like Big Data and Data Analytics can be attributed in part to Indian institutes of higher education like the Indian Institute of Technology (IITs) and the Indian Institute of Management (IIMs).

Applied data analytics is a hybrid beast

This makes it clear that not only is India a major data analytics hub–it is also evolving into a powerful educational destination for data analytics neophytes.

Analytics in the corporate sphere is a hybrid beast. By this I mean that it is an agglomeration of motley skillsets, including strategic planning and business development. In an entrepreneurial milieu, big data analytics has to serve an overarching, organisational imperative. It is typically used to answer questions such as: How best can our company grow? What systems and processes must we change/replace/reimagine to do so successfully? When/how do we change course?

To work within the parameters of such questions, the analyst must not only have a solid grasp of analytics tools; he or she must also have a keen understanding of the vertical/company in which they work, the industry at large, and wider socio-economic trends. Think of it this way: knowing how to use a scalpel doesn’t make you a surgeon—first you must understand human physiology, and its discrete pathologies and their treatment, before you can even approach a patient, scalpel at the ready.

In the case of our intrepid analyst, this would mean acquiring analytics skills in an interdisciplinary context from the get-go. When learning how to use a tool or device, it is best to do so in the interests of serving very specific ends.

The interdisciplinary data analytics eduscape

So what makes India’s analytics-friendly higher education landscape so exceptional?

India, it turns out, has an embarrass de riche of quality interdisciplinary Business Analytics courses. As home to a rapidly burgeoning array of training programs, the Indian post-graduate scene is geared towards producing analytics professionals with an understanding of business management.

Every year, Analytics Vidhya, a resource platform for aspiring and practicing data scientists and analysts, puts out two carefully curated lists: one of India’s top 10 Business Analytics programs specifically; the second list enumerates India’s best analytics training programs.

A significant number of the institutes on both lists have been accredited by globally recognised ratings agencies for their rigour and high quality.

Out of the 10 institutes that Analytics Vidhya deems to have the best Business Analytics course offerings in India, three are AMBA or Association of MBAs accredited (AMBA is a premier UK-based rating agency.) They are:

• The Post Graduate Program in Business Analytics – Great Lakes Institute of Management
• The Certificate Programme on Business Analytics and Intelligence – IIM Bangalore
• The Executive Program in Business Analytics (EPBA) – MISB Bocconi
• The Certificate Program in Big Data and Analytics (BDAP) – SP Jain School of Global Management (SP Jain’s Dubai campus is #10 on Forbes 2015 list of Best International MBAs.)

This demonstrates the importance these schools accord to the business management side of applied analytics. According to AMBA:

“It used to be that Indian students who wanted MBAs had to go abroad — often to countries like the US and the UK — to find decent business education through accredited business schools. But in the past few years, a number of business schools in India have been garnering international attention, as well as accreditation from AMBA, EQUIS, or AACSB.”

Number three program on the list—the EPBA programme–is a collaborative effort between Italian business school SDA Bocconi, (#7 on Forbes list of The Best International MBAs: One-Year Programs) and Jigsaw Academy, a leading Indian institute that offers courses in Big Data and Analytics, designed and taught by industry professionals. .

Another school (not on Analytics Vidhya’s list) is the Indian School of Management (ISB) in Hyderabad, which has both Association to Advance Collegiate Schools of Business (AACSB) and AMBA accreditation. (Only 5% of management institutions globally have earned AACSBs.) ISB offers courses in Business, Forecasting, and Web Analytics in addition to more traditional MBA fare.

The Indian analytics services sector has become a pivotal component of the global data analytics ecosystem. A key driver behind its ascendance has been its capacity to institute world-class training programs and facilities, allowing it to provide the Data Analytics market (at home and abroad) a skilled and seasoned workforce. And luckily there is no shortage of educational resources, which makes India one of just a handful of global destinations for students looking to learn applied data analytics.

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Who doesn’t want to learn about data scientist these days? The field is still hot, and the ample job listings for data scientists might make folks working in other fields instantly jealous. For young students, there are full degree programs and specialized courses to prepare them for the data-driven world but for those already in the field it’s not so simple. Going back to school is a huge and pricey ordeal. Thankfully, there are several online options. Whether you want to learn the basics for fun, sharpen your technical knowledge, or feel properly trained on specific platforms, there’s a course for you.

Choosing a course isn’t easy. It’s important to know exactly what goal the course should fulfill and what your limitations are.

Big Data University is the IBM-founded initiative based on the idea that education should be a right, not a privilege. The “data science for a social good” platform is designed to democratize access to useful data skills. Courses mostly range from two to ten hours and several are available in Japanese, Spanish, Portuguese and other languages. Courses are self-paced and mostly free. Big Data University offers a big data fundamentals course as well as several programming and database usage courses.

Code School. Coding is not the first thing that comes to mind when talking about data science, but it’s easily one of the most important pieces. Learning the right languages is absolutely paramount to succeeding in the field. Many beginner Data Science courses introduce programming languages but that might not be enough. Choose your weapon, presumably either Python or R, and get started with at least the basics. Note that, while many prefer the video-based learning style of Code School, there is also Code Academy which is work-based and completely free while Code Academy costs $29 per month.

Coursera is a popular MOOC (Massive Open Online Course) and home to the famed Data Science Specialization track, a nine-course program from Johns Hopkins University. While it is an introductory course, it’s not exactly beginner-friendly when it comes to statistics and algorithms. Coursera also hosts a Machine Learning course from Stanford professor Andrew Ng, one of the most regularly recommended courses online. These courses, however, do only start on specific dates. Luckily, Coursera has a huge breadth of other offerings, all of which vary wildly in duration, commitment-level and cost.

DataQuest and DataCamp are two often recommended and surprisingly comparable online programs designed to take users from zero to fully-prepped data scientists. The only glaringly obvious difference between the two programs is that DataQuest is often touted as Python-focused and DataCamp R-focused. DataQuest is also more comprehensive, appearing much like a typical university curriculum. Both platforms are similarly priced, DataQuest at $29/month and DataCamp at $25/month.

Educast, run by data storage company EMC, is a pricier option for those with specific needs. While there are some free courses, like one on data lakes, their focus is on paid options with video access starting at $600 and going up from there. Businesses looking to educate themselves or their employees may find specialized courses on Data for Business Transformation or Data Protection more than worth the cost.

EdX is a slightly different MOOC founded by Harvard and MIT. The nonprofit platform offers a lot of free courses from top universities. The Analytics Edge gets into the nitty gritty of analytics methods using R, and is a great free option for those looking to dive deeper than the typical “Intro to Data Science” courses. Other EdX courses look into topics that generic websites often gloss over like Marketing Analytics, visualizations, and education. Unfortunately, their courses do not run as regularly as on some other sites.

Explore Data Science was originally from Booz Allen, making it very special, being one of the few online programs attached to a hugely respectable consulting firm. The program is now run by Metis, a more classroom-based data science training company. This sort of notoriety also means the self-paced course isn’t necessarily cheap, at $99 for two months of access. Unlike free courses, however, this is a shiny and sleek program to get those with a basic proficiency in statistics, linear algebra, and programming into data science.

MapR may not be Cloudera or Hortonworks, but they’re still a player in the Hadoop world. More importantly, MapR Academy offer several short online courses for free as well as various certifications. Courses can be on demand or instructor-led. If, at the end of all your courses, you want to keep plugging through, you can check out their Certified Developer programs. They are also in the process of uploading a Big Data Essentials course.
Yes! Cloudera does offer some free video tutorials and webinars, like their Cloudera Essentials for Apache, but most courses cost several thousand dollars.

Udacity is an MOOC offering all kinds of courses for free as well as some with a small price tag. Udacity includes content created by professors, researchers and big name companies and reaches across the wide breadth of data topics. Their Data Analyst Nanodegree, however, is something a little more special. For those who want to get into data science but can’t waste several years on a specific degree, the nine to twelve month program is focused on learning useful skills and building a portfolio.

Udemy is yet another large MOOP boasting over 40,000 courses, both free and reasonably priced. It has its fair share of courses for data enthusiasts, including this incredibly popular MySQL Introduction. The course is a thorough, whopping 18 hours and is only one of several Udemy options on SQL. Users can also find shorter courses or courses on other specific data topics like using Tableau, data scraping and finding viral content.
Even though this seems like a lot of options, there are still more out there.

The Edureka platform offers more than one course on data science. CalTech, Stanford, MIT and Harvard all have their own unique programs to choose from. The Indian platform Jigsaw Academy offers a host of paid courses. There’s no shortage of options for those looking to get into the field. Choose your language and goal and get going.

image credit: University of Essex

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Finding and hiring a top-notch data scientist is a tough endeavor. The distinct skill set at the intersection of mathematics, statistics, information technology and business is rare to find and suitable candidates are well aware of their market value. Salary expectations well beyond $100,000 dollars are the new normal. Nevertheless, high expectations are oftentimes crushed and hired data scientists don’t perform as promised. Thus, pre-hire assessment of the candidate’s skill set is essential – is it time for a data science GMAT?

Ever since DJ Patil praised the data science profession as the “sexiest job of the 21st century“, usage of the term has reached inflationary dimensions. Many professions have rebranded themselves under the umbrella of “data science”, and everyone who is somehow dealing with data in their daily life tends to be called a “data scientist”.

Since there is still no common definition of “data science” and the associated skill set, the flexibility of the term is prone to exploitation. While companies with established data science teams have formed clear expectations regarding the skill and mindset of new recruits, companies without any prior data science experience oftentimes experience trouble in this regard. Consequently, assessing candidates in the recruitment process is only possible on a superficial level. Resume credentials, published and peer-reviewed research, GitHub repositories or prior work experience might serve as good proxies to determine the suitability of the candidate. But how is the candidate tackling real-life data science challenges? How can the prospective hire distill the problem and implement value-adding solutions? Currently, there is almost no possibility to check.

Every year, universities need to confront a similar challenge. Admission for graduate programs at top universities is not one dimensional, enrollment committees consider applicants from around the world and from a broad variety of academic institutions with different scientific standards. Assessing the suitability of a candidate cannot simply be narrowed down to a review of grades. In light of this situation, most universities require applicants to hand in GMAT scores.

Do we need a GMAT for data scientists?

Given the similar structure of the problem, why is there no such thing as a GMAT for data scientists? For companies it would provide great value, enabling them to quickly navigate incoming applications. Indeed, Facebook and Google support the Udacity nanodegree programs, which try to teach candidates the skills required for jobs in their tech departments. Graduates are (oftentimes) automatically eligible to apply and in some cases receive job offers straight after graduation.

This tendency underlines the structural problem in hiring qualified data scientists. If you have not seen them work on actual problems yourself, it is very tough to judge on quality and qualification. Companies with established data science teams – for example Soundcloud, Zalando and Amazon – have established the practice to give their candidates a real-life data challenge, which the candidate can work on to prove his or her skills. However, those companies are only able to compare incoming applications, lacking an overarching relative benchmark. Companies without an acting data science team face an even tougher challenge as they do not possess in-house capabilities to pose or judge the challenge. Consequently, a rather standardized assessment challenge to test data science skills would actually be of great value to both.

How would a “data science GMAT” look like?

As opposed to the university version, a “data science GMAT” would ideally resemble a real-world challenge. Just like consultancies try to test candidates in interviews with “case studies”, data scientist candidates should be confronted with a challenge, which requires the candidate to reveal precisely those skills and qualifications required in a real-job scenario. What does that mean? Essentially: structuring a problem, working with data, building a minimum viable solution, suggesting improvements. Take the famous LinkedIn “people you may know” recommendation engine as an example. Its implementation gave valuable new insights to the data scientists, allowing them to refine and optimize their models.

For data scientists, problems rarely come in a structured manner. Understanding challenges from a business perspective, narrowing them down to workable and testable questions is thus essential. Any challenge would describe a scenario in a detailed manner, requiring the candidate to distill the information for the most relevant. However, to enable cross-solution comparison, the data to work with and the the coding challenge should be clearly formulated and be focused on the standard “data science tasks” (classification, prediction, etc.), not breaching out to highly domain-specific knowledge – keeping in mind that the code needs to be evaluated regarding performance against a clearly defined set of metrics.

As a second component, the candidate should be asked to reflect on his/her solution. While the code oftentimes (if properly documented) reveals good insight into the candidate’s approach, it is only this qualitative angle, which makes the assessment holistic. Candidates should be asked to elaborate on challenges, pros and cons and possible ways to evaluate their solution. While it might seem obsolete to ask these questions (especially if a candidate has performed great in the coding section), it oftentimes reveals lines of thought, insights about the work ethics and habit of the candidate and thus relevant information in preparation for the hiring decision. Such a “technical essay” would, that goes without saying, have to be assessed by an actual person. That might certainly – as opposed to a multiple-choice GMAT – spike the cost of evaluation. However, relative to current salary expectations and opportunity costs this increased evaluation costs is still marginal in extent.

Can a standardized test really assess all the necessary data science skills?

Of course not. Just as the GMAT cannot assess the suitability of the candidate for graduate programs, a standardized test or challenge for data scientists cannot rule out that suitable candidates will fail or unsuitable candidates perform well. Given the broad range of skills required, any challenge will always be biased to certain skills.

While a somehow standardized test as outlined will certainly never be the universal solution to hiring a data scientist, it can still provide adequate guidance to help companies make more informed decisions. Especially for those companies, which do not have any data scientist in-house, it minimizes the costly risk of making the wrong initial hire. As long as there is still no such thing as a standardized data science assessment, companies should consult 3rd party support, whether through other companies with existing data science teams, service providers or independent institutions, in order to holistically assess their data scientist candidates with adequate and comparable challenges.

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In 2009, Hal Varian (Google’s Chief Economist) famously joked that “the sexiest job in the next 10 years will be Statistics”. Fast forward to 2016, and it’s abundantly clear that he was right (and how!)

$118,709 – the average salary for a Data Scientist today (according to Glassdoor). Compare that with, say, what the average web developer gets paid: $67,097.

Companies are churning out exponentially more data every day yet struggling to derive value from it. According to McKinsey, by 2018, the US alone will face a shortage of 150,000+ data analysts and an additional 1.5 million data-savvy managers.

But you know this stuff. You’ve heard the stats. In fact, you’ve even taken the important first step — firing up your browser and hunting down ways to learn Data Science. And for getting started, you deserve a pat on the back!

So where do you go from here?

Well, let’s not kid ourselves – learning these skills and then getting a job as a Data Scientist isn’t the easiest thing in the world. It’s not going to happen overnight and it’s important to know what to focus on.

Over the past year, we’ve talked to a lot of aspiring data scientists. The one question we hear a lot is:

“I’m an X major who wants to be a Data Scientist. Where do I start?”

So we’re sharing 5 important tips that will help you get on the path to Data Science excellence (and with some further reading on each topic). If you have your eyes set on the Data Science job market, these tips are crucial.

1. Develop an area of technical analytic expertise

Start with a solid foundation in statistics. Once you’ve built this expertise, learning Advanced Statistics, Machine Learning, or Natural Language Processing could come in handy. If you are still in school, take courses in these subjects. If not, develop expertise in any one of these areas, and try to be conversant in a few others. More reading – Learn How to Get Your First Data Science Job

2. Build an affinity for code

Hacking skills might be even more important than formal systems development here. As an entry-level Data Scientist, a lot of your work will be to take lousy data and put it in a form that can be analysed. And it will be different with each data set you work on. Learning Python or R will serve you well for multiple Data Science applications. More reading – Comparing Python and R for Data Science

3. Learn to tell a story (with data)

First, learn basic statistics. Second, be able to express your brilliant analysis in a way that normal people can understand. Your clients and colleagues won’t always understand what terms like “p-value” mean. You need to properly explain your results, their significance and their credibility (why SHOULD someone trust them) in a way that is straightforward and easy to understand for non-data scientists. Visualization techniques can be helpful in these instances. More reading – How to Tell a Powerful Story with Data Visualization

4. Get a mentor

A hands-on, project-based approach is best when you’re learning the ropes. You will make some mistakes and solving someone with experience to talk to, review your work, and keep you accountable makes it easier to stay on track. More reading – Nate Silver on Finding a Mentor, Teaching Yourself Statistics, and Not Settling in Your Career

5. Build a strong portfolio

Building projects is not just the best way to learn, it’s also a great way to showcase the skills you’ve acquired. The best companies will want you to demonstrate that you can work through a data problem end-to-end: from data gathering and cleaning, to analysis and clearly communicating your findings. An effective way to start building portfolios is to enroll in some Data Science competitions. More reading – Tips for Data Science Competitions

If you take these steps, expect to spend at least a solid three months to half a year doing them right. Learning new skills and developing relationships takes time to get right, but in the end, when you accelerate your data science career, it’ll all be worth it.

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Looking for the perfect podcast for your morning commute or during your downtime? Here’s a list of the best podcasts in data (in alphabetic order).

Data Skeptic
Unusual Angles

Data Skeptic takes a different take on how we review data—thanks to some healthy skepticism, listeners come out with unusual information and knowledge. The show alternates between interviews with industry experts, and mini­episodes wherein the host explains data science tidbits to his non­data scientist wife. The tone of this show is simultaneously intellectual and a bit off­beat. It’s fun, and easier to follow than highly technical podcasts. If you need a series with nice production quality and clear, friendly radio­voices, this may be the one.

Data Stories
Storytelling Through Data (and other more artsy topics)

For those that believe “data is beautiful,” this may be of great interest. Rather than focusing only on science, hosts Enrico Bertini and Moritz Stefaner discuss data visualization and storytelling. Featuring interviews, projects, and plenty of images on their website, Data Stories can be a bit more philosophical than other podcasts. For example, in one episode they discuss an outside project called “Dear Data.” This project involved “two women who switched continents get to know each other through the data they draw and send across the pond.” The show is both informative and a lively break from other highly technical, science ­heavy series. Some interesting sample topics include visualizing your Google search history, discussions on data art, and bridging academia and industry.

IBM Analytics Insights
High­Level and Highly Specific

This series is not everyone’s cup of tea. It can be very technical and, as the speakers are often big names with big products, they may speak about their own projects often. Still, for thosewho work in the fields of data, this podcast tackles some very specific topics, like zone architecture and telematics, but also more “hot” topics like big data myths and trends.

Learning Machines 101
Academic and Educational

The self ­termed “gentle introduction to Artificial Intelligence and Machine Learning” is very technical and has plenty of depth to offer. It can feel a bit like a narrated science textbook, allowing for a lot of information with a touch of storytelling. The well­ established Dr. Richard M.Golden uses each episode to address one topic at length. Luckily, Dr. Golden focuses specifically on including both beginners and those working on a higher ­level. Whether you’re a hobbyist,student, or scientist, you can come out of these podcasts with a real and deep level of technical knowledge.

Linear Digression
Short, Sweet and Silly

Katie Malone and Ben Jaffe of online education startup Udacity host this upbeat and accessible series on data science and machine learning. This podcast may not be ultra­ professional, but the hosts are very entertaining and informative. Through their discussions (which are often goofy) listeners get a peek into very specific concepts. They often discuss real­world problems and examples average folks may see in daily life (example: how can computers learn to tell jokes?). Each episode is relatively short, running between 8­20 minutes, making it easy to tune in.

Obsessive ­Compulsive Data Quality Radio
Highly Technical, Highly Informative

What exactly is data? Meta­data? International quality data? Big data? If data is your poison, this will be work and play simultaneously. This podcast is the audio accompaniment to ocdqblog.com, and is proud to be vendor­neutral. It’s good for data nerds, tickling several different topics and featuring several special guests and relatively easy ­to ­follow discussions. The series’ topics will be useful to business­ types, who want to utilize data in their own work. Topics often include data governance, master data management and business intelligence.

O’Reilly Data Show
Big Names and Big Business

One of the biggest perks of being a big name like O’Reilly means access to a lot of important and insightful guest speakers. Rather than just talking about big names like Cloudera, Apache Spark and Google data flow technologies, host Ben Lorica gets to talk with them in person. It can be very technical, but the speakers and topics keep each episode lively. Plus, the usefulness of the information makes it well worth the effort. Hearing from these important people adds a level of inspiration and depth to the entire show that is hard for smaller shows to rival.

Partially Derivative
Drunk Yet Informative

Hosts Chris Albon and Jonathon Morgan make data both fun and inspiring. From sports to art to space to “Can Killer Robots Marry Their Cousins?” this podcast has worked its way into peoples’ hearts around the globe. Oh, and they spend the entire episode drinking, making it particularly fun and easy to listen to. Plus, Partially Derivative’s production quality is comparatively high, with a clear focus on making their podcasts entertaining. They speak openly about cultural, social and any other kind of topic, and many listeners find this series to be particularly inspiring and relatable to life in general, rather than just in the lab.

Talking Machines
The Crowd Favorite

Many consider The Talking Machines the best data podcast around. The information and production quality of this show is top notch, and the hosts very professional. They discuss machine learning, and balance the complicated technicalities with extreme clarity. The podcasts are carefully constructed and delivered to make technical information accessible and usable. Hosts Katherine Gorman and Ryan Adams interview an array of industry names, and also discuss data in relation to different spheres, including economics, interdisciplinary data, and even video games. With its insightful discussions and precise answers, this high ­quality podcast is hard to top.

What’s The Point?
Politics, Economics, Society

Polling aggregation website FiveThirtyEight is behind the podcast What’s The Point? The series is incredibly deep and the discussions extremely insightful—possibly because they focus on topics like politics and how data is changing business, including its impact on workers’ understanding of their role in the workplace. Host Jody Avirgan does a fabulous job of really engaging with interviewees, making discussions thought­ provoking instead of one­ sided lectures.

Need more? Check out Not So Standard Deviations, Freakonomics Radio, or Data Driven Security

image credit: Patrick Breitenbach

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Preliminary research on how the Internet of Things will impact education may lead you to believe students will soon be connected to an iPad, RFID scanning objects and getting their own personalized curriculum delivered to their desk. It’s a dreamy new world of individually tailored lessons. It might be prudent to remember how computers were supposed to completely alter the way students learn decades ago. Yet anyone who took a “computer 101” class in high school may know tech in the classroom is not the futuristic bonanza we want it to be.

Teaching is not an easy job. Only half of the work involves class time and, depending on where that classroom is located, teachers may have very different objectives. Of course they want students to learn, but a much larger education system and government often dictate the “how.”

Many of the daydreams for IoT in education involve students taking advantage of new technologies to complete cool new projects. Students in science classes might use RFID to tag sample specimens in the wild so they can take notes without leaving the classroom. Textbooks could be scanned to receive instant additional resources and assignments. Despite the fact the IoT is above all else about creativity, these common suggestions do not do it justice. When textbooks came with CDs of additional materials and assignments, who even used them? This is the dead-end of IoT in the classroom. Once the cool factor is gone, it isn’t so revolutionary.

Connectivity Must Be Used Creatively

The truth is, connectivity in schools is about far more than making lives “easier.” Microsoft boasts that their newest products can start-up 80% faster, saving teachers time. This is fabulous, but that in itself does not mean a better education. Real changes will come from fostering a better—not faster—learning climate. Thus far, better connected computers have mostly been making the work of teachers easier. Teachers are able to save time finding, connecting and implementing new resources thanks to their connected technologies. But that is only the beginning.

Employees at Bosch have done their part to improve the school “atmosphere.” Climate control and energy saving measures are some of the places the IoT will hopefully be affecting students. Bosch has taken an image of Einstein and turned him into a visual representation of climate. When the temperature or air changes, so does Einstein. The product was tested at the Bundesgymnasium Dornbirn grammar school in Austria with great results. Students were always aware of their Einstein. It helped notify them and teachers about minor shifts, enabling them to always create the ideal atmosphere and focus better. Students and teachers spend some eight hours in school every day, and these minor shifts in weather can drastically alter the mood. Furthermore, the Einstein actively taught students about climate, and even gave them a chance to get involved. It’s creative, highly interactive and practical designs like these that will prove useful over time.

What about the non-techies? Those who don’t identify with extreme interconnectivity? English teacher Robyn L. Howton told Education Week how she prefers to let students gain experience through doing rather than listening. Her classes begin with a brief introduction and then the students are given tasks. Using iPads, groups begin preparing and creating presentations to be shared with the class. Though this is a huge step forward, it is is hardly the sexy over-connectivity we have come to expect in the year 2015. The class researches social topics, most notably the Ferguson protests, or related subjects that spark their curiosity. The ability of technology to bring the outside world so quickly into the classroom is one of its greatest powers when used wisely. Howton’s classroom, however, should also be taken with a grain of salt:

“I decided my personal goal was to turn my classroom into a model so other teachers who want to start down this pathway have someone to come and [observe].”

Not every teacher is the same, and not every class can function like Howton’s. That’s why developers and teachers will have to get creative. With a combination of obvious heroes like Smartboards and Google docs with new, niche technologies like Bosch’s Eintein and nerdy kits that teach how to code or engineer, teachers and students will slowly have access to all kinds of learning tools.

Technology Is A Life Skill

Others want to take the IoT in the classroom to a much higher level. They want to focus not on utilizing technology, but teaching it. Students will be early adaptors to new technologies. This is one primary reason that eight UK schools are running an £800k pilot program to anchor education in connectivity. Such programs are about preparing for the future, and creating minds that can move through the complex IoT with ease. Surprisingly, much of their funding went straight into creating an appropriate cloud. Connected tech creates ample data, and it is vital that it be easy to share, store and access. The program is also deeply rooted in the desire to share knowledge and data between schools. What one class learns could be shared with other students. The exact usefulness of such data could be unlocked through proper data analysis, or simply creating new channels to communicate and socialize in schools.

The UK hopes to eventually roll this program out to far more schools, but are currently stuck on testing and cutting back costs—the latter being one of the most dangerous parts of these endeavors. Finding funds to bring in effective changes will be a hard sell in many countries where schools are already facing plenty of funding problems. Furthermore, the divide between rich and poor areas is not going to be an easy or happy discussion when it comes to IoT implementations. It may be easier to get IoT into schools in the form of cutting energy costs. Better automation and monitoring can save money, supplying real-world proof that the results outweigh the costs. When the “results” come in the intangible form of a student’s (often untestable) knowledge, it will be much harder for cash-strapped schools to find funding. There will be no way to make this shift easily.

The IoT Will Shape Education…Eventually

Successful integration of the Internet of Things into the education system will come slowly, and in very nuanced ways. Some schools may use it to save money or harness data; some will prepare students to be highly tech-literate; others will find creative uses for their specific needs. The dream of personalized, detailed instructions and seamlessly interactive technology will run head-to-head with the funding issues as well as current test-based accountability systems. Shifting the focus on education to include the IoT will mean a massive shift in understanding what education means and the companies that benefit will be the creative minds that can create practical, reasonable products the teachers, students and administrators can get behind.

The US Government is in a fix owing to a rather old-school data collection system available with the Education Department that prevents quick retrieval of data on approximately 40 million Americans with student loans. The student loan portfolio amounts to $1.1 trillion, according to a report published by the Wall Street Journal last week.

The system is unable to produce data on defaulters with lowered payments, while also being incapable of data analyses on new loans.

According to officials, the key insight needed from the data is: “Why are Americans continuing to default on their student loans—even when their burdens are relatively small—at a time when the labor market and economy are improving?”

Earlier last month, the federal student loan relaxation was raised by $22 billion, as part of an annual accounting revision to update estimates of the projected costs or earnings of federal lending programs, Wall Street Journal reports.

To better gauge how effective schemes are- and to help distressed borrowers- Rohit Chopra, student-loan ombudsman for the federal Consumer Financial Protection Bureau, explains, “Much more needs to be done to get student-loan data to the level we need.”

“Given the explosive growth in student lending and the rise in student-loan defaults, increasing our understanding is critical so that we don’t repeat some of the same mistakes that happened in the lead-up to the mortgage crisis,” he added.

Denise Horn, an Education Department spokeswoman, pointed out that the system is still being developed through multiple phases over the next few years and will “more easily allow for timely, accurate, and consistent analysis of federal student aid data.”

The US government has been the primary lender of student debt since 2010, a role earlier commanded by private lenders (e.g. SLM Corp.’s Sallie Mae) and were only guaranteed by the government.

Photo credit: m00by / Foter / CC BY-ND

BBC has launched an initiative across UK to spawn a digitally aware generation that would be more proficient in coding, programming and digital technology.

The initiative, entitled Make it Digital, intends to address the requirement of 1.4m digital jobs in the next five years. “BBC Make it Digital is hugely exciting and will shine a light on digital creativity like never before,” said  Jessica Cecil, the Controller of BBC Make it Digital. “We are proud to partner with an amazing range of fantastic organisations across the UK, which will open the doors to new opportunities in the future,” she added.

BBC has partnered with around 50 organisations, across the UK to amplify the vibrant digital industry.’ Partners include corporations like ARM, Barclays, BT, Google, Microsoft, Samsung and educational institutions and organisations like Apps for Good, British Computing Society, iDEA, Nesta, among many others.

Working with these organisations BBC has created what is called a ‘Micro Bit’ – a small programmable hardware device – to be provided to year 7 children (age 11-12) across UK, one million in total.

The Micro Bit will assist youngsters get acclimatized to more advanced products like Arduino, Galileo, Kano and Raspberry Pi. Still in the pipeline, the device is slated for an autumn release.

5,000 young unemployed people will be made part of the Make it Digital Traineeship to enhance their digital skills and essentially make them job ready.

“A wide-reaching season of Make it Digital content across TV, radio and online will showcase how Britain has helped shape the digital world, raise awareness among mainstream audiences on why digital matters, and inspire younger audiences to have a go and get creative with digital technologies,” reports a BBC news release.

Photo credit: Lars Plougmann / Foter / CC BY-SA

Australia based e-learning startup OpenLearning has garnered AU$1.7 million in funding. The new funding will help to fuel the development and implementation of new MOOC-based business models, and stimulate growth in Malaysia, where OpenLearning has been appointed the official MOOC platform for higher education institutions.

Having invested $1 million, entrepreneur Clive Mayhew led the round while contributions were also made by ASX-listed ICS Global, Robin and Susan Yandle, and Hideaki Fukutake, the director of Japanese education company Benesse Holdings.

Mayhew praised how the platform is responsible for the evolution of the education industry and how students learn : “OpenLearning represents a massive opportunity to provide high-quality, accessible, and collaborative education to students around the world,” he said. “The platform is changing the way teachers provide education and the way students learn.”

Founded by Adam Brimo, Richard Buckland and David Collien in 2012, OpenLearning is a free platform for massive open online courses (MOOCs) and other online course providers.

It is reported that the fresh funding will also help develop the its engineering and instructional design teams, while design and implementation of new MOOC-based business models for commercial customers will also take place.

In a growing industry, that online education is OpenLearning can expect fertile ground ahead. A report published by cloud based e-learning platform, Decebo, the industry will cross $51.5 billion by 2016, with an annual worldwide growth rate over 2012-16 of 7.9 percent.

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On the surface, E-learning is simple enough to understand. A broad umbrella term that is used to refer to using technology in a learning, educational environment, E-learning could be a number of things; touch-responsive, interactive technological systems in a young classroom, online testing systems, web-based courses, online databases; they all qualify as e-learning systems.

The past few years have seen a markedly increasing use of e-learning methods, especially in two sectors: online courses/teaching or VLEs (virtual learning environments), and online, open-source information databases. Universities worldwide [my alma mater included] employ the use of Moodle, a learning platform that provides personalised learning environments to students. An open-source platform, students, educators and faculty can access personally, systematically tailored learning environments to access information related to reading material, courses, grading and notifications, with easy, simplified intra and inter-departmental communication streamlined by the interface. An extremely useful tool, it significantly simplifies the learning experience.

For Users: (Via Examining the Khan Academy Model)

With internet access increasingly permeating fields where resources are typically scarce (such as highly advanced or specialised qualification), e-learning tools can greatly help with practice, tutoring, honing specific skill sets or even teaching new ones altogether.

The popular Khan Academy, set up by Silicon Valley entrepreneur Salman Khan in 2006, provided micro-lectures via YouTube videos, exercises and educator tools to those who needed them, entirely free of cost.
The interface has since expanded, with the gamification of the website; a previously video-only set of tutorials is now interactive, with medals awarded to users during the learning process. This has both modernised the online learning experience and made it more of an open, fun experience for younger learners, while still providing necessary skills.

Videos by the Khan Academy, which initially focussed solely on Mathematics, now span a range of topics across the sciences, such as biology, cosmology, health and medicine.

For Coders and Developers- SCORM vs. xAPI

A lot of software used by the Khan Academy (and other e-learning portals) tends to be open source, which is extremely convenient for developers to build on.

The existing technical standard for e-learning software products, SCORM, is in place to indicate to programmers the degree to which their code can interact with e-learning software. ExperienceAPI (also known as xAPI and Tin Can API), a new e-learning standard designed to replace SCORM, is an e-learning software specification that allows learning content and learning systems to speak to each other in a manner that records and tracks all types of learning experiences.

More analytics-rich than its predecessor SCORM, xAPI means that e-learning is now available across wider platforms that are not as restricted. Performance tracking, already a feature in some e-learning databases, is now not restricted to browsers, finding support in desktop and native mobile applications.

Platform transition, working on an e-learning set via a certain platform (via browser) and being able to continue it via a native application, is another feature xAPI allows. xAPI is also much more specific than SCORM, allowing more granular tracking by developers, coders and users, resulting in a more analytical, data-rich experience across the board.

Ultimately, this benefits both user and developer, with each community able to effectively track progress, effectiveness and interaction via a single, open source software specification.

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Writer and communications professional by day, musician by night, Anuradha Santhanam is a former social scientist at the LSE. Her writing focuses on human rights, socioeconomics, technology, innovation and space, world politics and culture. A programmer herself, Anuradha has spent the past year studying and researching, among other things, data and technological governance. An amateur astronomer, she is also passionate about motorsport.
More of her writing is available here and she can be found on Twitter at @anumccartney.

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From apple grower to fine arts student, from software developer to machine learning PhD- Jose Quesada has done it all. Now, he’s established Data Science Retreat, a course to help people with his passion for growth and development to delve into the world of data science. We recently spoke to Jose about his remarkable story, the Data Science Retreat experience, and why so-called “soft skills” are often the making of future data scientists.

Give us a brief introduction to you & your work at Data Science Retreat.

I love developing people. When I see the ‘delta’ on a person after hard work, it makes me feel good. Data Science is ‘the’ place for this ‘huge delta’ development: because the state of the art is changing rapidly, you are forced to teach yourself new things every week just to stay current. Fields like this tend to attract people who like pushing themselves.

I came from a rural background. My father grew apples, and would expect me to do the same. Instead, I studied psychology and fine arts. Then I did a PhD with lots of machine learning. In it I developed a software system to teach pilots how to land commercial aircrafts without the need of a senior instructor sitting next to them (which I didn’t patent; silly me).

What you can see is that I changed direction many times; I taught myself mostly all I know that is really useful. I think we live in self-taught paradise. But after a certain level of excellence, it’s hard to make progress. This is something most aspiring data scientists find. No matter how many MOOCs you do, there’s a barrier that very few people ever break.

This is why Data Science Retreat started. I think I know how to create an environment where you can go “faster than average self-taught speed” and break the barrier of excellence that most people encounter. I asked myself: “What does it need to exist for this to happen?”. My answers was: you need to have access to ‘chief data scientist’-level people, contributors to leading open source packages, etc, and they need to be invested in your progress. You need to be surrounded by other people seeking excellence, too. DSR is the kind of setup that I wish I had when I started. Two batches later, all I can say is that I’m very proud of the result, as is everyone involved.

Talk us through the course structure.

You can check the instructional part online in our curriculum. What you don’t see there is how we approach the ‘portfolio project’, where you do original work under mentors.

We start with finding a good question. This is a creative process, and a skill you will use often once you graduate. Not all questions are answerable with data and machine learning. Of those which are answerable, not all of them produce business value. Once you know you have a good question, finding the data that can be challenging. Or cleaning it. Or making sure it’s correct.

Next step, you find a good evaluation metric (‘How do you know when you’ve won?”), and start iterating with your predictive models. When to stop fine tuning parameters is also a key skill; you will hit diminishing returns eventually.

Once you have demonstrated you answered the question you started with, it’s time to present your results and make a convincing case in front of stakeholders. Here, your communication skills determine everything: your beautiful product may never get put in production if you don’t do this well. You’ve exercised your communication skills quite a bit already by settling on the question; ideally the company is receptive, and was sold on the value. Do they believe you have generated that value, now that you’re finished?

At all times, you could have asked different mentors. You got around 270hrs of instruction on state-of-the-art methods. But let’s be honest: anything can happen here. It’s stressful. You are at the helm managing your project. Often you find your data has nothing going on for it, your predictive models are not doing anything interesting, or you cannot answer the question. You are back at square one. And there’s a hard deadline where companies will sit and look at you with their undivided attention.

What differentiates Data Science Retreat from other courses for aspiring data scientists?

1. Our mentors are at the ‘chief data scientist’-level or contributors to leading open source packages. All our mentors teach, and they are invested in your success. There’s nowhere else in the world you can get this today.

2. We focus on the question as much as on the technical details of the solution. We provide training on technical communication; you will present often, and get one-on-one feedback from a communication expert.

3. We prepare our participants for leadership positions. That is, either being the lead data scientist, or the only one in the company. This is far harder than preparing someone to join an existing group of data scientists and solve problems picked by someone else.

Why did you choose Berlin as the HQ?

There are two hotspots in the EU: Berlin and London.

Berlin has been doing really well in the last five to ten years with regards to Internet tech startups. When you look at figures in terms of size, how many VC-backed companies there are, how much venture funding flows into those companies etc. As a result the tech scene is huge in Berlin, there’s an interesting meetup almost every day.

London is also very interesting. There’s definitely money floating around because of so many banks. But tech-wise, choices are more conservative. If you are a bank, losing information, even if it’s a single transaction, is a big no-no. You have to stick to tried-and-true technologies. Berlin companies can afford to pick riskier, newer technologies, because they often deal with consumer-level information, which is usually not as crucial. If Twitter loses a tweet, it is unlikely they will get sued, unlike a bank. I suspect Berlin is already ahead of London tech-wise, and with time this difference will only grow. This is a good thing for data science, because companies who can take risks will use data scientists sooner than conservative companies.

What do you consider to be the main differences between the data science scenes in the US & Europe?

There’s of course a lot more VC money in the US, and this makes it easier for companies who use data science to exist. There are more web-scale, B2C companies in the US. 40% of the data science jobs are in the valley according to LinkedIn. And the pay is higher over there. So what’s to like about the EU?

• Since there’s less VC money floating around, companies doing well in EU (and hiring) are more likely to have solid business models (and be resilient to big changes in the economy).
• EU companies are less prone to follow fads.
• EU companies tend to offer better working conditions, even if salaries may be lower. Retirement, health insurance etc are all well covered by law. You get a full month of vacation. If you are on a high tax bracket, at least you know your money is not used for say fueling the military industry.
• There’s something to be said about being early days for data science in EU. There are better opportunities for truly outstanding people. From what I hear, there are in the order of 100 people able to do a good job at lead/chief data scientist in the entire EU. If you are one of them, or can imagine to be one shortly, you are clearly in a privileged position.

Still, I worry about EU competitiveness mid-term. Some companies are too traditional, and have trouble integrating data scientists in their structures. But this is a topic for another day 🙂

What are the essential skills and traits a data scientist must possess?

There are three must have skills to just enter the data science space. You need to know some programming of some kind preferably R or Python, but really any programming language will do. The second thing is that you absolutely must know some statistics and machine learning. This shouldn’t be a superficial understanding of these data analysis techniques – any programmer can blindly implement a technique as a black box. You need to actually understand why a particular technique is suitable and what its limitations are. Finally, you need to know how to query databases.

Different data scientists will have different strong suits. Some will be very strong with data visualizations, some with databases and others with statistics but all data scientists need to have these basic skills to work in this space.

We do not run coding tests, because nowadays with sites like stack overflow, it’s easy to write almost anything without really understanding the details. We consider coding tests non-discriminative. We do like to see code samples on github for existing projects.

We invite the most promising applicants to an interview. There, we make sure we are a good match for each other. There are questions about creativity with data, communication, and raw machine learning knowledge. We want to see people who have put the effort to learn this stuff on their own. Many interviews end early.

You’re currently accepting applicants for your third class; what level of prior knowledge do your candidates typically have?

You only need to know at least one programming language well. Other than that, there are no real prerequisites. We have applications from people who are already data scientists, but feel they are stagnating at work. Initial skillsets are all over the place, which makes is challenging (and fun!) to prepare the teaching. As you can see, the curriculum is very varied, and no participant has had experience in more than one or two topic groups.

A big chunk of people applying have been in the industry for years, and/or have PhDs. But I’ve seen many people with no experience, who weren’t so strong on paper, but ended up doing incredibly well during DSR. The sheer willpower and raw intelligence of some participants has been inspiring. I’m happy my interview process detected these people and let them in! I wish more and more people applied even though they felt intimidated by DSR’s reputation; if you know you have it in you, and have a burning passion for data topics, by all means apply! We tell you whether you are accepted the same day of the interview. If you are on the fence, I’ll encourage you to go for it; this batch we are hosted by Zalando, which is a great place because they have around 40 data scientists working already (two DSR alumni!).

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Capira Technologies has launched iBeacon powered app for libraries which provide patrons with a more personalised experience. Beacon Integration allows libraries with CapiraMobile Apps to interact with their patrons using indoor micro location services over Bluetooth. iBeacons are currently used in many retail locations to interact with customers, but are now being brought to libraries. iBeacons are small battery powered devices that broadcast wireless messages, with adjustable ranges as small as 1 foot and as large as 250 feet.

The features of this app include Circulation Notices which reminds patrons about due items; Event Notices which can be triggered when a patron walks into a particular section notifying them about upcoming events; Informational Notices; Shelving Notices enabling patron to see a list of items in a particular section; Patron Assistance and Beacon tracking amongst others. Unique features include a library card sign-up module that enables users to provide proof of residency remotely by taking a photo of a utility bill, and a digital library card presentation module that allows patrons to display and scan their library card barcode using their mobile device.

Managing Partner and Lead Software Engineer of Capira Technologies, Michael Berse said- “This is a new way to personalize a patrons experience at the library. For example, patrons can now be reminded about items due that day they may have forgotten to bring with them when they arrive at the building, or walk into a specialty section such as Local History and see information about it on their device. He added that the service allows libraries with limited staff resources to provide more customer service.”It’s just another way to really keep in touch with patrons,”

“This isn’t an opt-out system; it’s an opt-in system,” Berse says.

The company is considering partnering with nearby businesses, such as restaurants and train stations, so that patrons can get special messages and incentives inside and outside of the library.

Developed by Apple in 2013, iBeacon app was set to transform the retail industry. While it has been used extensively for selling products, it is now being used to enhance library and museum experiences. Spotzer, a Boston based statup has worked with Neue Galerie in New York and the Boston Atheneaum to make the user experience more personalised.

Brendan Ciecko, CEO and founder of Spotzer quoted “This particular technology really ties together what makes libraries and museums so valuable to the world. They’re an indelible, invaluable physical venue for knowledge.”

iBeacon Integration is currently in beta testing at the Somerset County Library System in New Jersey, and the Half Hollow Hills Community Library in New York. Long Island’s Jericho Public Library (JPL),Hampton Library, and Mattituck-Laurel Library(MLL) each recently launched customized versions of CapiraMobile.

MLL’s Assistant Director, Jeffery Walden stated “The app just has a more modern connotation than ‘go to our website,’” he said. “It’s appealing to the type of people who live with their phones. When you say ‘website,’ they’re thinking the old way, where you’re going to have to blow everything up to look at it, and not everything is going to work properly. With the app, we’ve advertised it as the digital branch of the library—the place to go where you can do things easily with your mobile device.” (source: theDigitalshift)

This technology will reinvent the rapidly declining importance of libraries and museums in our community, reminding people of the wide range of services and resources they have to offer.

Read more here.

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Data Analytics has found its way into the US education system where data silos containing performance data of former students are being analysed to predict the outcomes of current ones.

According to a Time report approximately 125 schools around the U.S. have been collating and sifting through years of data covering millions of grades earned by thousands of former students, as part of this effort; something enterprises, retailers and businesses have been doing with available data points to glean customer behaviour in order to provide better services and upscale revenues.

“What we used to do, and what other universities do, is let the C student go along until it was too late to help them,” notes Timothy Renick, Georgia State’s vice president for enrollment management and student success. “Now we have a flag that goes off as soon as we spot a C in the first course.”

A predictive algorithm has been established to track students’ chances of dropping out or graduating and alert the academic advisors about the ones who may fall short, so as to help them before it’s late.

In the wake of the economic hitch starting in 2008, there has been a noted drop in graduation rates. Georgia State University, one of the institutes incorporating the technique, has analyzed 2.5 million grades of former students to predict outcomes with current students. Since 2012 the “early warning system” is attempting to fix the “lower-than-the-national-average graduation rate,” having flagged 34,000 students last year who might suffer a setback.

The Time article points out that apart from the graduation rates going up there is also a resource and monetary advantage: “tracking data in this way keeps tuition coming in from students who stay, and avoids the cost of recruiting new ones, which the enrollment consulting firm Noel-Levitz estimates is $2,433 per undergraduate at private and $457 at four-year public universities,” it said.

Read more here.

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Amrut is helping change the way India learns. Re-imagining what learning should be. He’s committed to destroying the one-size fits all system and replacing it with personalized learning, empowering the learner to shape their learning and not be a passive recipient.

The big debate – Data vs Intuition

How many times have we, as parents, looked at the work our children do, taken the time to assess if they’ve really understood the point of the lesson, and then approached a teacher to do things a little differently for our child?

We’ve long been proponents of the fact that data can change everything. In every field, feedback, has become a vital part of service delivery – allowing entire organizations to adapt their processes, change their delivery methods and allow for the maximum utilization of resources. Technology today also allows us to get that data, if not real-time, then as close to it as one can get. We’ve tried our best to apply data in all our delivery, even to the last mile – the student teacher interaction and what we’ve learnt has been startling.

The role of the teacher is changing.

At this point of our proposition, we always hear this outcry – The teacher! The teacher is the most important link in the chain! It is the teacher who provides companionship, guidance, a true learning environment! How can you replace that with something as cold as data! Ugh.

Let us explain.

The reality is that each child learns differently – and no one model works for all children. The ability to have intelligence on each child, to process that data, and to use it in a way that allows for the child to learn in the way most comfortable to them is something that is sorely lacking in our education system. We believe we have cracked some part of this problem through our innovative system of collecting information on each child. We use a model that we’ve developed in-house, that allows teachers to input all sorts of data into a Development Diary– from assessments to the smallest observation. This contributes hugely to our lesson plans, milestone developments etc. (We hope to take this model digital soon, making our time to market much faster than our current 2 weeks)

The way our pre-school program is designed also helps tremendously. It allows each child to interact with various stimuli – in a controlled environment – through the day. Our skill stations (each station focuses on a different skill – Language, Cognitive etc) are actual physical environments that allow each child to learn at this own pace, leaving the teachers enough time to observe, assist and record – for each child. Each expression of a skill is tightly documented. And it is the teachers job to push children, each of them , to the best they can be. This also has the advantage that we’re now able to hire teachers based on their skills (interaction with children) rather than only on the basis of knowledge they hold in their heads.

The key things to keep in mind when building a data driven approach to learning are –

1. The richer the better – When building a data driven model, the more data you have from the word go, the far likelier you are to end up with the data you eventually need. There is no such thing as superfluous data.
2. Speed – The sooner you can get data, the speed at which you correct bugs, the speed at which you adapt and deploy becomes critical to your final output.
3. Flexibility – Don’t build systems that are so rigid you cannot change them with ease.
4. Data isn’t everything – Don’t lose sight of the fact that your product is being delivered by a teacher. With years of experience, a teacher’s intuition – gut feel – cannot be discounted. Your data may point one way, and your teacher another. Go with the teacher. You wont fail.

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Consulting and technology outfit Infosys have today announced they helped UCAS deliver academic results and university places to 400,000 British students back in August.

For those unfamiliar with the British higher education system, UCAS is the platform solely responsible for notifying students whether they achieved a place at their University of choice. On results day- which was the 14th August this year- most students will have spent their mornings anxiously refreshing the UCAS homepage, waiting to hear if their dreams of higher education were to become a reality.

As you can imagine, UCAS required a robust back-end system to deal with the level of demand. To put it in perspective, the platform had to upload and deliver a staggering 5 million sets of results, and handle 239 logins a second at peak demand. Many students from abroad also apply through the UCAS system, meaning the platform had to handle requests from across the globe on a multitude of devices.

Elaborating on the technological demands, Rajesh K. Murthy of Infosys stated ““With hundreds of thousands of students receiving their results on the same day, there is a massive spike in demand on the UCAS Track service around the world. Each applicant needs to be able to log in quickly on results day to see their status, and be able to use the Clearing process if they are still looking for a place. UCAS and Infosys have worked closely to ensure the systems are ready for students to take the next important step in their life.”

UCAS’ Director of IT, James Munson, added that although most of the UCAS results and “clearing” (placing students who didn’t achieve the required grades into institutions they didn’t originally apply for) happened on the day, the system was designed and implemented months in advance. “Months of advanced planning with the Infosys team have enabled us to deliver our A-level results day Confirmation and Clearing service, meaning hundreds of thousands of students were able to log in that morning to see if they had secured their place at university or college.”

Such a high-profile use case is certainly a coup for Infosys, proving their mettle in managing mission-critical systems on a high-intensity workload.

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We live in an increasingly connected world with more capabilities than ever before at our fingertips, and it’s making many jobs out there a lot more dynamic. Education is no exception to this trend, though teachers and administrators will likely use these online tools for much different purposes compared to private businesses. Even so, online technology gives teachers the ability to hone their skills and reach students in increasingly effective ways. These advantages, however, do come with a downside. As more teachers do much of their work online in the cloud, the risk for security problems grows. And since schools work with valuable student data, the possibility of a security breach is very real and potentially disastrous. 

There are a number of ways student data could be compromised through teacher accounts. The one most people have heard of is hacking. This essentially means an outside attacker has infiltrated the system and has gotten unauthorized access to the teacher’s account. From that account, a hacker can get further access to data and personal information. Another way student data could be affected is by having a teacher get locked out of his or her account. Most of the time, a lockout is a defensive measure taken by the system when it detects suspicious activity. It can be helpful when an actual threat is detected, but if it’s triggered by mistake, data loss and other leaks may result. 

With these threats posing real security question for schools, teachers need to know how best to protect their personal data along with data of their students. That protection can only start when done with their own accounts. One area where teachers should place more focus is in making their passwords stronger. Far too often, people will overlook the importance of their passwords and how vital it is in keeping attackers from hacking into their accounts. A strong password can frustrate hackers and make them look elsewhere for an easier target. But what makes a strong password? The first technique is to make a password that is at least eight character long or longer. A password should also avoid common words or terms that are related to the account user’s life (so no birthdays, places of birth, or pet names, for example). Passwords also need to contain numbers, capital letters, and symbols, making them that much harder for hackers to guess. All teachers should use different passwords for every account they have. That way, if one password is cracked, the other accounts won’t be compromised. 

Another area teachers should reexamine is their online behavior. When logged into their school account, teachers may end up browsing the internet. When doing this, teachers need to make sure to only go to websites that are secure. They should especially avoid suspicious websites, since unsecure websites have a greater chance of downloading malware to the user’s computer, which may in turn spread to the rest of the network. With the recent discovery of the Heartbleed bug, only using secure websites on a school account is more important than ever. Secure sites take advantage of encryption, which is represented by an “https” address and the graphic of a padlock in the address bar. 

Backing up data is also an important strategy for anyone aiming to protect valuable information. Many people may choose to do this by saving data to the cloud, and while Cloud computing does have benefits, additional backups should also be made on a physical hard drive. Data can also be sent to a separate, safe account. However teachers do it, the important thing is that a backup of the data is at the ready in case an account is compromised. As an additional benefit, backed up data can also be used to recover quickly from an emergency or disaster unrelated to security breaches. In any case, regularly backing up account files is a must for teachers looking to protect student data. 

Security breaches have unfortunately become a lot more common in recent years. While the headlines may focus on major corporations, schools are still a target for cyber attacks. Protecting student and teacher data isn’t just a matter of common sense, it’s a privacy issue. If teachers make greater efforts to secure data, they’ll have more confidence that information will be kept safe and be able to fully utilize the wonderful tools available on the cloud. The overall effect will be a better education for students of all ages.

Rick Delgado- I’ve been blessed to have a successful career and have recently taken a step back to pursue my passion of freelance writing. I love to write about new technologies and keeping ourselves secure in a changing digital landscape. I occasionally write articles for several companies, including Dell.

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I recently helped out at an access initiative for Oxford University, and the experience got me thinking about how we could bridge the state-private divide in Oxbridge and the Russell Group schools.

According to Professor Les Ebdon, director of the Office for Fair Access (OFFA), the biggest challenge facing fair admissions right now is that “many teenagers from poorer backgrounds do not see Russell Group institutions as suitable for them”. Many access programs try to combat this psychology of inhibition, but often to little avail.

For example, the scheme that I was involved in offers high-achieving students from non-selective state schools a chance to hear for themselves what Oxford admissions really is about, but a problem lies in this approach: since its demographic pool targets teacher-nominated students who have already achieved satisfactory grades, the benefits it offers are of an informative, rather than a developmental, nature. A better alternative, suggests a study by the Institute of Education, would be to “intervene earlier to ensure that those from poorer backgrounds achieve their potential during their school years”.

This is where ‘Big Data’, with its principle of inferring probabilities from vast amounts of information, could ‘intervene’ as a solution. In their New York Times bestseller Big Data: A Revolution That Will Transform How We Live, Work and Think, Mayer-Schonberger and Cukier point out a fundamental shift in mindset that ‘Big Data’ has brought forth: “We no longer focus on causality, but instead we discover patterns and correlations in the data that offer us novel and invaluable insights.”

If we apply this recalibrated focus to access in higher education, then the question that matters should change from “Why do certain students not apply to Russell Group universities?” to “What are the possibilities and indicators of all students succeeding in these universities?” While there is nothing wrong with using the former to jumpstart an access scheme, the latter will help discover more prospective applicants by looking at data en masse and making new connections from it.

So how can we mobilise the potential of Big Data to unearth the potential of ‘diamond in the rough’ students? The answer could lie in something close at hand and accessible to all – phone and social networking apps. 

An Egalitarian Model: Big Data as the Talent Scout

In Learning with Big Data: The Future of Education, the authors cite the example of Luis von Ahn, the creator of Duolingo, as someone who manages to glean meaningful insight from ‘data exhaust’, which refers to “data that is shed as a by-product of people’s actions and movements in the [cyber] world”. A computer science professor, von Ahn was by no means an expert in pedagogy, and yet he was able to find out how and what people learned which languages best, all from observing the digital trail ‘left behind’ by users, which included the time lapse between responses to questions, grammatical errors, repeated mistakes etc.

Duolingo’s success is instructive: Incorporate this method within a secondary school curriculum, and there is no reason why similar merits will not play out. Imagine if schools were to use an app like Goodreads in their English lessons: students from as early as 12 could participate in a form of educational ‘life logging’ by tallying and sharing with their peers books that they have read weekly, in effect creating a virtual ‘Book Club’ for the classroom through the datafication of effort. Whoever gains access to this information may then derive insights about a student’s academic potential: an avid reader of Shakespeare is likely to be a strong contender for studying English at top-ranking universities, while someone else who has poked around with Friedman or even primers on Aristotelian philosophy may attest to a capability for the PPE degree. If the agency with this information passes it on to the university access branch, then the next step could be a form of targeted access which circumvents the common problem of self-filtering. Equipped with this knowledge, access officers could send relevant information pamphlets to these students, thus giving the reluctant souls a much-needed nudge of encouragement.

Moreover, ‘letting the data speak for itself’ also increases impartiality in the selection process – there is no favouritism at stake, and a Dickens-loving recusant won’t be any less disadvantaged than a Chaucer-skimming sycophant when time comes for the access people to spot academic potential. In the realm of business, predictive analytics means mapping patterns from the transactional data of customers to identify risks and opportunities; in the world of education, this logic similarly applies by discerning potential from the behavioural data of students to widen the possible pool of top university applicants.

This is especially significant for students who are talented in the humanities, as while capability in the hard sciences or math is more measurable by test scores, quantitative evaluation often does not do justice to a student’s potential in the arts. After all, just because one fared poorly in a history essay does not mean that he or she does not have the potential to succeed at degree level, especially when much of university study is grounded in critical thinking rather than rote learning. Thus, within the higher education framework, Big Data is able to infer the probability of academic success and gauge academic interest, therefore truly taking up the role as a background-blind mediator with its power for predictive arbitration.

A Matter of Trust: Personal Privacy vs. Social Equity

Yet what follows is a burning question: who exactly is to ‘algorithmise’ all this data? There are two options: either a third-party data analytics company steps in, or the company which owns the educational app could employ algorithmists to do the job. More crucially, who is to absorb the cost of this additional service? Will the government, the Sutton Trust, OFFA etc. collaborate to support what is still a fledging initiative? Or will this push adaptive learning firms with existing data collection infrastructures like Knewton and Amplify to venture into a risky but possibly lucrative terrain?

This uncertainty is reinforced by the failure of InBloom, a non-profit US organisation which, despite having had the financial backing of the Gates Foundation and Carnegie Corporation, closed down after a mere two years due to widespread backlash over student privacy concerns. With data-related fiascos such as WikiLeaks and the News of the World wiretapping scandal still raw in the minds of many, it is little wonder that there remains a general anxiety, if not outright paranoia, about data collection and its relationship with privacy protection.

What differentiates my proposed access initiative from the InBloom precedent, however, lies in the nature of the data source. It is important to remember that InBloom handled sensitive data such as a student’s health records and a family’s tax returns – information that, if fallen into the ill-intentioned hands of hackers or marketers, could well endanger the interests of not just an individual, but an entire family. Arguably, analysing the digital footprint of student-users on an interactive learning app carries implications that are a lot less grave. Indeed, some may use the slippery slope argument, expressing the concern that this seemingly innocuous ‘first step’ may legitimise more sinister forms of tacit data-tracking down the line.

Such worries are not unfounded, yet if we consider the tremendous benefits that could come out of taking this leap of faith, then perhaps we’ll concede that sacrificing a bit of our personal privacy is but only a small price to pay for the great advantage of increasing social equality in education.

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Jennifer Chan is currently an English Literature finalist at the University of Oxford. She has taken up many editorial roles, having been the online editor of The Oxford Student, one of the two major newspapers on campus; the economics editor of The Oxonian Globalist, an academically-oriented journal on international affairs; and the editorial assistant of the 2014-5 Oxford University Careers Guide. Jennifer is passionate about exploring the relevance of Big Data to the ‘every man’, gauging the function that data analytics may play in the equalisation of education, and familiarising the public with this concept through examples drawn from daily life.

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Online learning management system Desire2Learn Inc. recently scored $85 million in its second round of funding. With the new funding, Desire2Learn intends to expand international outreach and invest in the development of its cloud-based learning software.

“Unlike advertising and e-commerce, education is an industry that has just begun to use big data analytics,” notes Jon Sakoda, partner at New Enterprise Associates which took part in the new financing round. “Ultimately, Desire2Learn is helping educators deliver personalized learning the way that Amazon.com delivered a personalized shopping experience.”

Brightspace is D2L’s main platform that enables teachers deliver content to students in the classroom and at home via the Web and mobile devices, while tracking their learning progress regularly instead of just once a semester, at the time of a final exam or standardized testing.
The cloud-based software can be used by an individual teacher for free or can be used with more sophisticated analytics, content and other tools, by an entire school, school district or national education department.

Presently, 1,000 schools and more than 15 million students are using the cloud based software. New Zealand has adopted the Brightspace platform for its public K-12 schools, Chief Executive John Baker, as have several U.S. states, cities and districts. Approximately, 10% of D2L’s earnings is through helping corporations train their employees. The new funding will help the company grow that piece of its business.

According to Baker, the technology functions like a “Netflix for education,” recommending courses based on their skills, interests and aptitude which helps convert users to students with a complete degree within four years and decreases dropout rates.

Read more here.

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Data Origami, a new startup focused on developing learning techniques, tools, libraries and common solutions that data scientists and analysts can use everyday through hands-on-keyboard videos, has recently launched its first series of screencasts. The aim of Origami’s project is to help users develop a more profound knowledge of data science on which they can base well-informed decisions.

“I think the most important thing is understanding uncertainty,” The founder of Data Origami, Cam Davidson-Pilon explained recently in an interview with VentureBeat. “It’s not believing what your eyes see. Too often, people make inferences — not even doing statistics, but just, like, mental inferences that are wildly off.” This uncertainty could be resolved, by devoting some time to watch the start-up’s screencasts. As the demand for data scientists is increasing, there’s many options to get into the field, such as UC Berkeley’s Online Master’s program, open online course sites like Coursera or training programs in cities like San Francisco, Berlin or New York. Data Origami supplies its users with the necessary knowledge to later get into new tools, designed to improve collaborations and speed in data science.

Each month the start-up releases two new screencasts on Python libraries, including Patsy, Bayesian A/B testing, and other topics, created by Davidson-Pilon. A subscription, which includes access to all screencasts as well as applicable code and data, is $9 per month and the subscriber numbers have been promising since the launch of the website.

There is the potential of more content being produced by Data Origami in the future, venturing into other topics such as SQL for data analysis, or or the open-source Spark engine and tools for data processing. Davidson-Pilon, who has also written the e-book “Probabilistic Programming and Bayesian Methods for Hackers” (in collaboration with other authors) and invented Lifeline, a Python library for survival analysis has also mentioned plans to expand the website’s services through cloud-based technology. So far, users work with the screencasts and downloaded IPython notebooks that contain the lessons. Despite the success, David-Pilon is not planning to quit his day-job as the product analysis team lead of commerce company Shopify just yet. But once he can start to work with self-acquired knowledge on more specific topics and there’s more clients involved, things could become more interesting.

Read more here.

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IBM announced yesterday that it has embarked on a major collaboration with China’s educations system to focus on addressing the burgeoning Big Data & Analytics (BD&A) skills opportunity in the country.

The initiative, named IBM U-100, will see the tech giant donate a range of BD&A software worth $100 million and “provide expertise to support 100 universities in China to create the next generation of data scientists at three levels”: 1) set up BD&A Technology Centers in 100 universities, 2) launch undergraduate and, 3) graduate programs in 30 universities and create a Center of Excellence at five universities.

By mid-2105, when the programme is expected to be rolled out to all 100 universities, the aim is to see 40,000 students per year gain expertise in BD&A. The company said that the courses will be delivered by professors, IBM researchers, engineers and IBM Global Services consultant.

“IBM is privileged to extend its collaboration with the Ministry of Education and universities in China,” said D.C. Chien, chairman and chief executive officer (CEO), IBM Greater China Group. “Together we will be able to accelerate the nurturing of skills in Big Data and Analytics and help prepare future business leaders to apply BD&A technologies to tackle complex societal issues, from health care to transportation and public services.”

Although IBM’S sales in China have slumped since last year – which, according to Reuters, has been caused by the backlash against the revelations that United States has been spying on foreign subjects and governments – CCID Consulting forecast that the big data technology and services market in china will grow to $8.7 Billion by 2016.

The recent announcement by IBM is an attempt to narrow the gap between the expected growth of big data in the country and the lack of skills needed to manage this.

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The World Bank Group and its private-sector lending arm, the International Finance Corporation (IFC), are trying to harness big data’s potential to support national education systems, with their recently launched ‘Systems Approach for Better Education Results’ initiative, which collects and shares comparative data on educational policies and institutions from countries around the world.

Jin-Yong Cai, the executive vice-president and CEO of the International Finance Corporation, writes that big data “can provide teachers and companies with unprecedented amounts of information about student learning patterns, helping schools to personalise instruction in increasingly sophisticated ways”

Bridge International Academies, an IFC client founded by American entrepreneurs, tests different approaches to teaching standard skills and concepts by simultaneously utilizing two versions of a lesson in a large number of classrooms in Kenya. Exam results are recorded, with more than 250,000 scores logged every 21 days; from these data, Bridge’s evaluation team determines which lesson is the most effective and distributes that lesson throughout the rest of the academy’s network.

Information technology offers the right tools to broaden access to high-quality, affordable education, that will provide countries with the skilled, talented young people necessary to spur economic growth. Students’ performances are subject to many individual factors that can cause them to decline, but by gathering results on a large scale, variables flatten out, and the important differences come to light.

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From schools and universities to libraries and museums, public sector organizations are constantly battling with budget restrictions. The trade-off between offering better services with reducing costs is always a challenge, especially for those in the public sector. Indeed, considerations like this have played a crucial role in the emergence of Business Intelligence in the public sector.

Although BI can be used to improve public sector performance in general, the role of BI is starting to gain momentum in educational institutions in particular. With reliable information, schools are becoming aware of the usefulness of ERP software in tracking student examination results, submission of assignments, teacher absences, student-teacher satisfaction rates and even comparing course grades with other schools in the country. With this information, teachers as well as parents can assess the areas that need attention – whether it’s with the syllabus, teaching method or absence rates.

One company that promotes use of ERP software in education is an India-based company, Foradian. Through its plugin architecture and API model, Fedena allows schools to develop integration with other software, while also enabling schools to add plugins to meet their needs and enhance the features on the ERP software. Fedena School ERP software capitalizes on the vibrant ecosystem surrounding their product – from freelancers to resellers — and schools can leverage the expertise of open source enthusiasts to support the changes they need for their schools.

Although the company is helping schools across India – 15,000 education institutions are powered by Fedena’s ERP software in Kerala alone – it has also been received globally with 40,000 schools and colleges using the software worldwide. Currently, Fedena’s open source attracts 200+ downloads a day and has 65000+ downloads in total. Moreover, Fedena’s work has not gone unnoticed. The company has received a number of awards in recent years for their innovation in the education sector – Edustars, which is supported by Accel Partners, awarded Fedena a prize for being the “best education start-up from India.”

With education leaders trying to think creatively about maximizing performance through streamlining their processes, technology and people, ERP software solutions will become a popular source in securing these goals. While there are a number of companies offering similar products, Fedena’s cloud servers and well-priced software is a shining example of how the public sector – and especially schools — can use ERP and BI to increase efficiency, reduce costs and ultimately reach their desired targets.

Foradian is a company headquartered in Bangalore that was founded by six friends in 2009. In 2010 they launched their first product called Fedena. Fedena is a multipurpose school and campus management software which is used by thousands of educational institutions in India but also in Africa for all their administration, management and learning related activities.

Sqrrl, the company that develops secure NoSQL database software for Big Data applications, announced that it would be collaborating with Macmillan Education Australia to help them power a next generation education portal. Sqrrl Enterprise, the company’s NoSQL database, will allow Macmillan to secure and protect huge amounts of data that can only be accessed in authorized ways.

“We are very excited to be working with Macmillan in Australia,” says Sqrrl CEO Mark Terenzoni. “Big Data apps for education have strong security requirements, and Sqrrl brings unique Data-Centric Security capabilities to help make these apps possible.”

Sue McNab, the platform technical lead at Macmillan Education Australia, also commented on the partnership.

“For Macmillan Connect, we require a NoSQL database that can support interactive queries, process very large amounts of multi-structured data, and provide fine-grained access controls and encryption…we looked at a number of options, and only Sqrrl could provide these combined capabilities.”

This article is based on an excerpt of  Learning with Big Data: The Future of Education by Viktor Mayer-Schönberger, Kenneth Cukier.

Big Data in Education

What if data collected during your journey through education was collected and stored? What if the records would forever show that you had to take a class to catch up on math skills in college? Or even already in school? Universities could potentially tap into this data and optimize their admissions so as to attempt to inflate the later grades at graduation.

Viktor Mayer-Schönberger, Professor at the Oxford Internet Institute, and Kenneth Cukier, Data journalist at The Economist, consider this to be a perfectly plausible future scenario. Already today they register calls for a more transparent usage of a student’s transcript. Adaptive learning algorithms, for example, could then both identify the weaker students and then induce them to quit early on to improve later overall performance.

The value Big Data in education may bear

Clearly there are many advantages to adding a quantifiable element to education, giving both the student and their teacher tools to more specifically improve their performance. But for every advantage there are also risks.

For fear that their children’s data would be saved forever, parents managed to halt a Gates Foundation backed initiative to store educational data in six out of nine states chosen for an initial launch. Since big data in education could also include information on sick days, visits to the counselor or even the depth of understanding reached for a given book, the ability to recall specifics on all these metrics bears a threatening potential for many. The inability to shed our past thus bears dangers because it prevents us from outgrowing past mistakes that were part of our learning experiences: no aspect of our development would be under the radar anymore.

 Protecting the student’s privacy

In many countries privacy protection laws exist specifically to prevent any such nasty surprises. The idea is that creators of data could opt-in to the positive uses of collecting this data (such as individualized learning) without the negative consequences. Unfortunately the true allure behind Big Data is the secondary use of gathered information: the possibility to find some meaning in data that was collected for completely different purposes. As a result, any opt-in that was consented to is hardly able to foresee the uses that the collected data will eventually have.

In response to this fear the EU and the US have already started discussing the possible ways of placing the burden of protecting the data collected on individuals on those wishing to use it for secondary or tertiary purposes. The onus of preventing any misuse of the data would therefore be on whoever collected and stored it.

The ultimate question is how the tradeoff between the dangers and the opportunities Big Data in education can bring can be navigated. Many positive examples of improvals in the individual learning experience already exist. At the University of Arizona implementing a software designed to help students graduate increased the percentage of students passing onto their next years of studies from 77% to 84%. More examples of this nature are most certainly desirable but they should not move us into a direction where our past rather than our will determines our future.