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5 Things You Should Know About Adaptive Learning

When we think about popular software, we picture sites like Amazon, Netflix, and Hulu: the pros of personalization.

As Amazon users shop, they buy everything from toilet paper to college textbooks, dog food, and nutritional supplements depending on lifestyle and AI-generated product recommendations.

As viewers watch Netflix, they stream endless hours of reality television or hard-hitting dramas or true-crime documentaries depending on personal preference and, more often than not, previous search requests.

These sites don’t discriminate based on age, interests, intellect or socioeconomic status, and neither does adaptive learning, an educational model changing the landscape of learning from a one-size-fits-all atmosphere, to a customized tool for students of all ages. Regardless of learning ability and prior knowledge, adaptive learning helps any student willing to learn.

To help you better understand the basics and benefits of adaptive learning, we’re breaking down a few must-knows.

  1. Adaptive learning helps teachers as well as students
  2. Adaptive learning engages the area between a student’s comfort zone and frustration zone
  3. School and Universities across the country have already adopted adaptive learning and personalization techniques:
  4. When it comes to adaptive learning, BE PICKY
  5. The U.S. Department of Education is investing in adaptive learning.

There are endless benefits to adaptive learning technology – students can track improvement and develop a sense of personal responsibility for their academic progress, students who’ve fallen behind on a subject have additional resources to draw from, and teachers can develop lesson plans tailored specifically to the classroom of students before them.

The adaptive learning model even extends beyond binge-watching platforms and educational models. AL has been implemented to improve training techniques within NASA and branches of the U.S. military, boasting revolutionary success and changing the way we approach educational improvement.

Invest in adaptive learning and whip your students in shape – NASA-style.

Here is a full text of the article.

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Hiring Manager – Centered Design of Adaptive Deep Learning

This post is my “How To”comment on the article Should Higher Ed Re-Design Its Own Re-Design?”
“Start from the end” is known as a goal-oriented approach. It does not contradict with a “personalized, student-centered” approach. Both can work together and reinforce each other, for example, in our Job-oriented, Student-centered Adaptive Deep Learning-Authoring Platform, www.itutorsoft.com
First, a Hiring Manager is supposed to specify the job down to specific tasks, performances, and competencies. Manual specification is very labor consuming and error-prone. Our Authoring tool can significatly simplify this process, save, display, and verify current results, as well as assure quality of outcome.
Second, the detailed job specification (tasks, performances, and competencies) can be used by Instructional Designers as a scaffolding for design of authentic curriculum, courses, and lessons, including interactive multimedia, VR, AR, and a real work place, which altogether assure backward knowledge transfer to competencies. Such design is supported by our Authorng tool as well.
Third, Learners are welcome to study those curriculum, courses, and lessons preferably by using our Personalized Adaptive Deep Learning Engine, which assures high efficasy and learning success.
Fourth, Learners get hired by that Hiring Manager.
iTutorSoft will be happy to help you with tools and getting started.
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What is Learning Engineering?

This post was inspired by the publication “The Need For Learning Engineers (and Learning Engineering)“.

Below is our comment that represents our vision and our Learning Engineering practice:

In general, Engineering is supposed to apply well-defined scientific knowledge, formal/mathematical theories. Such theories have precisely formulated terms and definitions, axioms and theorems, laws, frameworks, models, methods, criteria, …
In contrast, Learning Sciences are still ill-defined, empirical, and not formal/mathematical theories. They are more like Art. Their main method is probe and trial, find what works, reuse and refine it.

Meanwhile, it is possible to upgrade this Art of Learning Sciences with Wisdom of Interdisciplinary Methodology and Systems Approach, Order of Exact Sciences, Power of Artificial Intelligence, and Intelligent Tutoring Solutions.

On this rock-solid foundation, we, at iTutorSoft, have defined a general model (framework/engine) of Adaptive Personalized Deep Learning for its specific practical applications with unlimited accuracy and efficacy at scale. For easy mass implementation of our solution we developed a cloud technological platform CLARITY. Now the users of our platform can do, what is called, Learning Engineering.

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Why C-Levels Need To Think About eLearning And Artificial Intelligence

Just because ‘Every dollar invested in online training results in a $30 increase in productivity’ (via ).  Is not it amazing?

By Steve Olenski

I will dispense with any amenities and cut right to the chase. When it comes to corporate learning and training the numbers are truly staggering.

• Spending on corporate training has grown to over $70 billion in the United States.

• As one of the top three non-financial motivators, 76% of employees want opportunities for career growth.

• 87% of millennials say professional development or career growth opportunities are very important.

• 68% of workers say training and development is the most important workplace policy.

84% of global executives ranked employee learning as important or very important.

• 40% of employees who receive poor job training leave their positions within the first year.

•  Every dollar invested in online training results in a $30 increase in productivity.

• Companies that use e-learning technology achieve an 18% boost in employee engagement.

Trust me, there’s a lot more where this came from meaning there is no shortage of stats and research that speak to the benefits of e-Learning.

You are welcome to see the original text in here.

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The Disruption of Digital Learning: Ten Things We Have Learned

I am reposting this excellent work by  to provide you with the priceless modern vision of digital enterprise learning and emphasize its main future points, which are Intelligent Learning and Personalization. Enjoy!


Over the last few months I’ve had a series of meetings with Chief Learning Officers, talent management leaders, and vendors of next generation learning tools. My goal has been simple: try to make sense of the new corporate learning landscape, which for want of a better word, we can now call “Digital Learning.” In this article I’d like to share ten things to think about, with the goal of helping L&D professionals, HR leaders, and business leaders understand how the world of corporate learning has changed.

First, as a preview, let me explain why this topic is so important. The corporate L&D industry is over $140 billion in size, and it crosses over into the $300 billion marketplace for college degrees, professional development, and secondary education around the world. Thanks to the emergence of digital content and tools, all these programs are being reinvented for digital access, enabling businesses and employees to learn like never before.

Second, this topic is now the #2 topic on the minds of CEO and HR leaders. The 2017 Deloitte Human Capital Trends research discovered that 83% of companies rate this issue important and 54% rate it urgent up 11% from last year. In this world of automation, business transformation, and continued obsolescence of skills, companies are realizing that delivering on a compelling, digital learning experience is critical to business success.

Continue reading here.

 

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Without CLARITY platform, the current personalized learning efforts will, at best, lead to modest improvements

Without a technological breakthrough (read: without CLARITY platform), the current personalized learning efforts will, at best, lead to modest improvements in the execution of common place ideas (using data to drive instruction, executing leveled small group instruction, investing children in goals, etc.). School will look the same and be a little more effective and pleasant for all involved.

This is fine and the world is in many ways built on modest improvements.

But for personalized learning to live up to its hype (as well as to its philanthropic investment), it will need a technological breakthrough.

Read full article “Personalized Learning is a Transformative Idea Without a Transformative Technology

In addition to it, take a look at article “Differentiation Doesn’t Work“.

The reason is obvious for everybody: one teacher cannot differentiate instruction for very different students in the class. Nevertheless, this obvious reason has been thoroughly researched and finally proven. (wasting time?)

The conclusion is also obvious: only one-on-one teaching (which is tutoring) is necessary for differentiation and personalization of instruction. Not realistic? Right!

The realistic solution  is a right intelligent tutoring technology, which empowers teachers to serve each student of the class personally.

That is what we offer, actually.

 

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iTutorSoft demonstrated its technology at MIT Enterprise Forum “Artificial Intelligence Getting Down To The Binary”

It’s nearly impossible to tune into the business news without hearing about Smart Cars, Smart Homes, and Smart Companies that are increasingly reducing costs, reaching more customers, and replacing employees with algorithms and robots. Businesses are scrambling to take advantage of the opportunities that will come from the increased efficiencies, capabilities, and opportunities occasioned by artificial intelligence (AI). From healthcare to social welfare; from education to advertising; and from manufacturing to financial services, AI is poised to change the world as we stand at the brink of what is being referred to as The Fourth Industrial Revolution.

iTutorSoft demonstrated its technology platform at MIT Enterprise Forum “Artificial Intelligence Getting Down To The Binary”
 


 

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What is a learning architect?

“When building a house, one often turns to a traditional architect for assistance. The architect initially meets with the client to gain a thorough understanding of the project. With an eye on the big picture, he or she focuses on the challenges and goals that inform the assignment and envisions possibilities. The architect works closely with the client when drafting ideas, employing a collaborative and iterative process. When the final design is chosen, the architect serves as the liaison with the skilled craftspeople tasked with building the structure.

In the L&D field, the learning architect adopts a similar role and brings similar expertise to the table. Like a traditional architect, a learning architect possesses the requisite design know-how, but is also a strategic partner dedicated to helping the L&D leader develop and execute a sound, cost-effective plan. The learning architect is a problem solver, with strong consulting and project management skills. He or she is a skilled communicator with a flexible and agile approach. Finally, he or she has a solid understanding of analytics and can interpret data that will ultimately form the foundation for future projects”.

You are welcome to read more from this article, but it does not provide a solution of the problem.

Sure, learning architects are absolutely necessary for modern Enterprise Learning. But knowledge of behavioral psychology, learning design, and analytics are not enough to do this job. Learning architects are supposed to fill the talent gap between business executives and instructional designers, connect business with learning, reveal required learning from necessary business change, and evaluate impact of a learning project on business indicators. No human is able to do it without a new culture supported with a new smart tool.

We, at iTutorSoft, have developed such a tool. It supports all above mentioned functions, as well as design of Enterprise Adaptive Learning. Read more.

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Adaptive Learning: Five Common Misconceptions

Adaptive learning brings together the latest in learning science, data, technology, and workplace principles. The concept is based on the realization that people develop in their own unique ways and, therefore, require a more customized experience than an academic, one-to-many approach offers. By using multidimensional data and cutting-edge technology, L&D can deliver the right information to the right person at the right time, thereby promoting value to the individual as well as the overall organization.

Adaptive learning is a bleeding-edge topic for corporate L&D. As such, there is plenty of room for misinterpretation and misunderstanding about what it takes to deliver an adaptive experience. In order to shift your L&D team from a one-size-fits-all approach to a right-size-fits-one mentality, you must overcome these misconceptions and clearly articulate the value of adaptive learning within your organization. After all, your stakeholders don’t want to hear about learning strategy. They are focused on getting their employees to the desired level of capability as quickly as possible in order to drive business value.

Read more

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Pearson vs iTutorSoft about science-based learning design

This post is about the methodology of Adaptive Personalized Learning. It represents my comments on Michael Feldstein’s post that Pearson Releases a Significant Learning Design Aid.

It is not a secret, but rather a commonly forgotten or ignored fact, that scientific knowledge has a hierarchical structure in contrast to amorphous/messy heuristics, practices, pragmatic principles, rules, etc. A few of general interdisciplinary sciences, such as Systems, Control, Activity Theories, are on the top of this hierarchy. They generalize objects, models, and methods of more specific exact sciences and soft humanities, both located below in the hierarchy. In turn,  humanities include the learning sciences. In contrast to exact sciences (with well-defined objects, models, and methods), some humanities and all the learning sciences have an ill-defined, ill-observable, and ill-controllable object, the learning process. As a result, models and methods of learning sciences are often represented with amorphous/messy heuristics, best practices, principles, rules, etc. This is what Michael’s post is about. It is pretty challenging to analyze, understand and explain this mess to others who has the different mess in their minds. Michael’s talent is required.

The common problem is that we do not see a forest behind the trees. We are constructors without an architect. But according to the science hierarchy that forest, big picture, general models, methods, and methodology exist on the top level and can be found in such multidisciplinary theories as Systems, Control, Activity Theories.

It is not just an idea. In my R&D, I successfully used many interdisciplinary theories as a basis for developing general models and methods of cost-effective Intelligent Tutoring, Adaptive Learning Systems, and Platforms. According to the general-specific hierarchy,  all Pearson’s specific principles can be implemented within our general content-independent platform.

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