3 Ways Elearning Is Disrupting the Education Industry

Whether in a formal or informal setting, the ability to conduct research and impart knowledge has always been a crucial part of human life. That led to the establishment of colleges that are now centuries-old and have grown to become institutions. Those institutions might have seemed permanent and too dominant to challenge just a year ago, but the events of 2020 have caused a seismic shift in how people live, and how we learn specifically, such that digital education is clearly going to be the dominant force in the years to come.

This represents a lot of change in the sector, from the kinds of tutors who will be able to succeed in the new model to the tools the students and their teachers will need for an efficient process. That change also represents an immense opportunity for entrepreneurs — to make a profit while making a real impact on the future of humanity.

1. Interactive media

An important aspect of elearning is the ability to use a variety of media in communicating information. While traditional education models were largely restricted to text, and in some cases, audio and video, new digital learning platforms can leverage advanced technologies such as virtual reality (VR) and augmented reality (AR) to create a more immersive learning experience that will help students understand and retain more of the information which they are taught.

It’s also particularly helpful for training, where students gain experience using complex equipment without the risk of making real-life errors. Flight training for pilots and surgery models for surgeons are two increasingly popular applications.

A recent study by showed that education is No. 4 on the list of sectors receiving the largest amount of VR-related investments, shaping the industry to be worth over $700 million by 2025. That would amount to an increase of over 500% in the next five years. As large as those numbers are, they are unsurprising when we consider that up to 97% of surveyed students stated that they would be interested in undergoing a VR educational course. The market will always go where the money is.

2. Flexibility and on-demand education

This is another primary driving factor of the elearning revolution, and it is visible in every aspect of the system. From the fact that students can attend and participate actively in class from anywhere in the world using teleconferencing software, to on-demand classes that allow students to set their own schedules and learn at their own pace, the key is to provide a learning experience that is as tailored to the needs of each student as possible.

This flexibility means that students have full control over their learning process, thus making them more likely to stick to it. Corporate organizations have also been making a push into elearning as part of their training processes. In fact, 41.7% of global Fortune 500 Companies were already using some form of digital training as far back as 2013, and that number has only continued to grow.

This flexibility also reflects in the marketing approach. While traditional institutions are more uptight and reserved, entrepreneurs in the digital learning space can be more engaging by implementing content marketing strategies to attract users, such as the Learn a Course Online course reviews section which helps online learners share their experiences with online courses to help others make the best choices.

3. Artificial intelligence

Education has traditionally been driven by the and student, progressing based on their interactions. Nowadays, advances in artificial intelligence and machine learning/neural networks have resulted in software capable of evaluating how a student is doing based on a variety of criteria and guiding them accordingly to ensure they understand what is being taught fully.

These tools use everything from how students answer quizzes to how long they spend on a page and how many times they look back at certain sections to produce personalized learning plans just for that particular student.

This level of granularity in the education system is unprecedented, giving students the ability to direct their lessons and entire learning experience with the assistance of advanced AI. When combined with the removal of time constraints and a strict curriculum, it is clear why there is such a huge interest in digital learning among teachers and students alike, and why the industry is so ripe for disruption by savvy entrepreneurs.

By: Ademola Alex Adekunbi / Entrepreneur Leadership Network Contributor Founder of Tech Law Info

Source: 3 Ways Elearning Is Disrupting the Education Industry

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What is the Future of Online Education?

Expectations From the Education Sector

Education at scale doesn’t have to suck. If you ditch conventional e-learning’s clicky gimmicks, and focus instead on science-backed design principles and powerful human stories, your training will shift from tedious to transformative. Dr. Aaron Barth, thought-leader and president of Dialectic, gives progressive leaders the confidence they need to tackle their hardest people problems using scientific methods. Rooted in education, Dr. Barth founded Dialectic after completing his Ph.D. in Philosophy at Western University, keen on fusing theory with application.
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NEWS AND TRENDS 3 Ways Elearning Is Disrupting the Education Industry Digital education, driven by advanced technologies, represents […] Interactive media An important aspect of elearning is the ability to use a variety of media in communicating information […] Flexibility and on-demand education This is another primary driving factor of the elearning revolution, and it is visible in every aspect of the system […] Corporate organizations have also been making a push into elearning as part of their training processes […]
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What AI Practitioners Could Learn From A 1989 MIT Dissertation

Child at laptop

More than thirty years ago, Fred Davis developed the Technology Acceptance Model (TAM) as part of his dissertation at MIT. It’s one of the most widely cited papers in the field of technology acceptance (a.k.a. adoption). Since 1989, it’s spawned an entire field of research that extends and adds to it. What does TAM convey and how might today’s AI benefit from it?

TAM is an intuitive framework. It feels obvious yet powerful and has withstood the test of time. Davis started with a premise so simple that it’s easy to take it for granted: A person will only try, use and ultimately adopt technology if they are willing to exert some effort. And what could motivate users to expend this effort?

He outlined several variables that could motivate users, and many researchers have added to his list over the years, but these two variables are the ones that were most important: 1. Does it look easy to use? 2. Will it be useful? If the learning curve doesn’t look too steep and there’s something in it for them, a user will be inclined to adopt. Many researchers have added to this foundation over the years. For example, we’ve learned that a user’s intention can also be influenced by subjective norms.

We’re motivated to adopt new tech at work when senior leadership thinks it’s important. Perceived usefulness can also be influenced by image, as in, “Does adopting this tech make me look good?” And lastly, usefulness is high if relevance to the job is high.

TAM can be a powerful concept for an AI practitioner. It should be front-of-mind when embedding AI in an existing tool or process and when developing an AI-first product, as in, one that’s been designed with AI at the center of its functionality from the start. (Think Netflix.) Furthermore, AI can be used to drive adoption by levering TAM principles that increase user motivation.

Making AI more adoptable

With the proliferation of AI in sales organizations, AI algorithms are increasingly embedded in tools and processes leveraged by sales representatives and sales managers. Adding decision engines to assist sales representatives is becoming increasingly common. A sales organization may embed models that help determine a customer’s propensity to buy or churn, recommend next best actions or communications and more. The problem is, many of these initiatives don’t work because of a lack of adoption.

TAM can help us design these initiatives more carefully, so that we maximize the chances of acceptance. For example, if these models surface recommendations and results that fit seamlessly into reps’ tools and processes, they would perceive them as easy to use.

And if the models make recommendations that help a sales person land a new customer, prevent one from leaving and help them upsell or cross-sell when appropriate, reps would perceive them as useful. In other words, if the AI meets employees where they are and offers timely, beneficial support, adoption becomes a no-brainer.

We also see many new products and services that are AI first. For these solutions, if perceived ease of use or perceived usefulness are not high, there would be no adoption. Consider a bank implementing a tech-enabled solution like mobile check deposits. This service depends on customers having a trouble-free experience.

The Newark airport’s global entry system uses facial recognition to scan international flyers’ faces. It’s voluntary, and the experience is fantastic. The kiosk recognizes my face, and a ticket is printed for me to take to the immigration officer. Personally, I find this AI-first process a better experience than the previous system that depended on fingerprints, and now I will always opt for the new one.

Using AI to drive adoption

And perhaps counter intuitively, what if AI was used to drive elements of TAM within existing technology? Can AI impact perceived usefulness? Can AI impact perceived ease of use? Consider CRM. It has been improved and refined over the years and is in use within most sales organizations, yet the level of dissatisfaction with CRM is high and adoption remains a challenge.

How can AI help? A machine learning algorithm that uses location services can recommend that a rep visit a nearby customer, increasing the perceived usefulness of their CRM solution. Intelligent process automation can also help reps see relevant information from a contracting database as information on renewals are being entered. Bots can engage customers on behalf of the representatives to serve up more qualified leads. The possibilities are numerous. All these AI features are designed to ensure that CRM lives up to its promise as a source of value to the sales representative.

Outside of sales, consider patients. In the past few years, many new technologies have been introduced to help diabetics. Adoption of this technology is critical to self-management, and self-management is critical to treating the disease. For any new technology in this space, patients need to see that it’s useful to them.

AI can play a role in gathering information such as glucose levels, activity and food intake and make recommendations on insulin dosing or caloric intake. Such information gathering could go a long way toward reducing the fatigue that diabetics feel while they make countless health and nutrition decisions throughout the day.

AI’s algorithmic nature makes it easy to forget that it’s another technology and that it can aid technology. Its novelty can convince us that everything about it is new. TAM holds up because it’s intuitive, straightforward and proven. While we boldly innovate a path forward in the world of AI, shed convention and think like a disruptor, let’s keep an eye on our history too. There’s some useful stuff in there.

Follow me on Twitter or LinkedIn. Check out my website.

Arun provides strategy and advisory services, helping clients build their analytics capabilities and leverage their data and analytics for greater commercial effectiveness. He currently works with clients on a broad range of analytics needs that span multiple industries, including technology, telecommunications, financial services, travel and transportation and healthcare. His areas of focus are AI adoption and ethics, as well as analytics organization design, capability building, AI explainability and process optimization.

Source: What AI Practitioners Could Learn From A 1989 MIT Dissertation

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[2102.02437v1] EUCA: A Practical Prototyping Framework towards End-User-Centered Explainable Artificial Intelligence
arxiv.org – February 10
[…] It serves as a practical prototyping toolkit for HCI/AI practitioners and researchers to build end-user-centered explainable AI […]
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DIU
http://www.diu.mil – February 9
[…] xBD is currently the largest and most diverse annotated building damage dataset, allowing ML/AI practitioners to generate and test models to help automate building damage assessment […]
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Workshops List (AAAI-21) | AAAI 2021 Conference
aaai.org – February 9
[…] (AAAI-21) builds on the success of last year’s AAAI PPAI to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy […]
1
What is Responsible AI?. “It’s not artificial intelligence I’m… | by Yash Lara | Analytics Vidhya | Jan, 2021
medium.com – February 8
[…] But there is something called as ‘Ethical AI Practitioners’ […]
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Playing games, gamification, and the gulf between them
[…] Today, AI practitioners have a rich inventory of hundreds of games, with a myriad of variations […]
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What I Learned From Attending TWIMLcon 2021 —
jameskle.com – February 8
[…] There was a wide range of both technical and case-study sessions curated for ML/AI practitioners […]
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A Startup’s Journey Towards Artificial Intelligence With AI101 | by Jojo Anonuevo | The Startup | Jan, 2021
medium.com – February 7
[…] I highly recommend these for those who want to be AI practitioners and those tasked to build a team to help them understand the skills needed to recruit and interview […]
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“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI –
research.google – February 6
[…] In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA […]
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AI is Only Going to Get Smarter. How people are already “cyborgs”… | by Michel Kana, Ph.D | Feb, 2021
michel-kana.medium.com – February 5
[…] a critical mass of AI practitioners […]
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Home
mesumrazahemani.wixsite.com – February 5
Karachi.AI is a premier community of Applied AI practitioners. Founded in 2017, the community has staggering 4000+ members from wide variety of domains.   The unique diversity embodies our vision to educate masses towards Artificial Intelligence and upcoming Machine First era, where Jobs of the future will change drastically. ​ Our vision carries around three pillars of execution: 1. Awareness 2. Engagement 3. Empowerment
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2021 will be the year of MLOps
[…] The implementation of MLOps and closer collaboration of software developers and AI practitioners will bring a maturity to the market in 2021 […]
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What’s with the “Cambrian-AI” theme?
cambrian-ai.com – February 4
[…] Hence, I created Cambrian AI Research, where investors, media, and AI practitioners can keep up with the latest AI innovations, and communicate their plans and innovations, with 100’s […]
0
HPE data science experts help customers navigate the new AI accelerator landscape
community.hpe.com – February 3
[…] AI practitioners want competitive alternatives to CPUs and GPUs […]
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Hands-on Guide to AI Habitat: A Platform For Embodied AI Research –
analyticsindiamag.com – February 3
[…] Unlike the strictly algorithm-led approach of such traditional AI practices, embodied AI practitioners try to first understand the working of biological systems, then develop general principles o […]
1
Projects To Know – Issue #67
eepurl.com – February 3
[…] that occur due to data quality issues arising from technical debt) through interviews with 53 AI practitioners across the world. They find that AI practitioners are not properly incentivized to address data quality problems – instead, they are motivated t […]
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AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment | Professional Education
professional.mit.edu – February 3
[…] Communicate your value proposition to stakeholders Receive practical experience from the “voice of AI practitioners” across various industries Formulate a strategic vision and development plan focused on AI products […]
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Designing Ethics Frameworks for AI with Dr. Willie Costello Tickets, Thu, Feb 11, 2021 at 4:00 PM
[…] about AI ethics education, and has designed original courses, workshops, and frameworks to help AI practitioners learn how to think critically about the ethics of their work […]
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Artificial Intelligence: Week #4 | 2021
sixgill.com – February 1
[…] Notable Research Papers: Connect with AI practitioners of all levels Stay connected with artificial intelligence and machine learning practitioners around […]
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RCV at CVPR 2021
sites.google.com – February 1
[…] policy implications to Consider while constructing representative datasets and training models by AI practitioners […]
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Project Manager — Village Data Analytics (VIDA) | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
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Software engineering intern / Working student / Master thesis | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
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[Proposal] Ocean Academy: Project Oyster �� – Round 2
port.oceanprotocol.com – February 1
[…] series targets business people and organizations dealing with data, data architects, scientists and AI practitioners […]
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Environmental data justice
http://www.thelancet.com – February 1
[…] there is growing pressure from a community of researchers, activists, and artificial intelligence (AI) practitioners to make Environmental Data Justice (EDJ) a top priority […] One overarching and fundamental concern in the data justice field is the ability of data and AI practitioners to decide what and whose knowledge and data is counted as valid, and what goes ignored an […] As industry and governments increasingly look to AI practitioners and researchers for the solutions to important societal issues, understanding the systemic an […]
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