Author: Vladimir Goodkovsky

Authors of this article frame personalized learning as “a set of ideas for solving [the] problem” of teachers being overloaded with work and not having enough time to give their students individual attention. That is a feature, not a bug. By framing it this way, we hope to open the door to questions like the following:

• Why are faculty so overworked that “being the kind of teacher that [they] want to be gets harder and harder”?
• How does the increasing diversity of our student population make good teaching more challenging and what is the best approach to meeting that challenge?
• Are we providing faculty with the right support, incentives, and training to promote good teaching?
• When is personalized learning good teaching practice and when is it cover for bad labor policy?
• How can technology help us increase access to education without hurting quality and what are the limits of that capacity?

Reed the full article

In Spring 2016, faculty, support staff and administrators at Oregon State University met to candidly share their experiences with adaptive learning technology. Below are the main points of interest in my opinion.

1) Language is a barrier to adopting adaptive technology. Too often edtech providers use technical jargon that doesn’t resonate with those who are implementing their products.

2) Adaptive Learning technologies presented by vendors for consideration have been very different. That confused faculty, support staff and administrators.

3) It’s critical to engage faculty in the decision-making process, giving them time to explore multiple products and compare them, and identify those that are the best fit for the school and the students they serve.

4) One of the biggest barriers to usage is the problem of vendors over-promising what the products can achieve and what research backs up product design.

5) Institutions are looking for partners—not vendors—when it comes to implementing new solutions.

Read more

The post with this title has been recently published in EdSurge

I have analyzed it and distilled 12 key points of the post. Then I tried them on to check if our Adaptive Learning Authoring Platform CLARITY is right and provided my comments (in parentheses) for your consideration.

1) do not adopt a narrow and outdated educational model. (That is very important. We use a general and innovative model of the educational process)
2) do not define your Objectives only as facts, techniques, and procedures to be delivered and explained by instruction and then practiced to mastery. (Actually, it is up to our authors, they are free to define quite different kind of Objectives)
3) to not engineer out the Serendipity and curiosity (We do not even try, why? Use the blended learning principles)
4) do not naïvely develop and naïvely apply algorithms (We are not so naïve to do so 🙂
5) do not use wrong kind of learning tasks (It is up to our authors, they know right tasks to match their objectives)
6) do not view education in terms of “content” to be covered, acquired, mastered but as an experience. (Absolutely agreed! Instead of “content” we use an Activity, which covers “experience” as well)
7) do not apply your algorithms poorly (Yes, that happens often. We provide Quality Assurance)
8) use real world mathematical problems to teach (Yes, why not? Use blended learning, if you cannot do it in e-learning settings)
9) teach rich and powerful set of general metacognitive skills (Yes, why not? Define your Objectives correspondingly)
10) do not focus on one particular formula, method, a single answer, with “right” or “wrong” the only possible outcomes (Yes, it is a poor thing. We provide more options for our course authors)
11) focus on the valuable 21st century skills of holistic thinking and creative problem solving (Yes, we support it in our platform)
12) Students should be able to explore problems on their own until they discover the solution. (Yes, why not? It is not prohibited in Adaptive Learning. Use blended learning principles).

So, looks like our CLARITY platform could be blessed by the post authors. We knew it is right, it allows making right adaptive courses! http://www.itutorsoft.com

What about an Adaptive Learning Platform of your choice?

AI is all about learning. It is software that learns. It is also software that can create good learning content. We have to pay attention to what is happening here. They are achieving things that were unimaginable just a few years ago. They are learning about learning by creating effective learners. The fact that AI is having so much success by following the lessons that cognitive science has to teach, is surely a wake up call for learning theorists, especially those still stuck in foggy world of social constructivism. Reed more

As an educational model, differentiated instruction has been around since the time of Socrates. As a method of providing a classroom of students, often with differing abilities, instruction based on individual aptitudes for learning, it has been an effective alternative to repetitive rote memorization that is still widely practiced in many countries around the world. Today, with the widespread availability of new learning software and platforms, differentiated instruction can take on a revolutionary role under the guise of adaptive learning, and it could alter our thinking about education and the way students learn. Read more.

This is an interesting post by Elaine J. Roberts, Ph.D.

Where I tried to clarify some technology matters:

Elaine, I like your vision, concerns and beliefs about ideal learning. But let me specify a bit your generalized perception of adaptive learning algorithms. We already have around a range of adaptive learning systems (algorithms) and they are quite different.

Some of them (like Knewton) really dictates the learner what to do and people (learners and teachers) have no choice or power to change it. That is what you do not like, right? Though Knewton is good for drill and practice for test passing.

Others (like Smart Sparrow) give people (teachers) all the power to program a course of learning (and providing choices for learners). That is what you might like, but quality of such manually designed courses is usually questionable. It is not good either.

An ideal adaptive learning system (algorithm) is supposed to provide:

1) course authors with a strict logical framework for shaping amorphous human-authors content ideas, excluding errors, assuring quality and yet leaving a lot of space for creative freedom;

2) learners with close monitoring of personal learning behavior to collect necessary data

3) learners with opportunity to drive their own learning

4) Learners, educators and authors with explicit representation and automatic frequent updating of a learner’s personal proficiency, learning style parameters and preferences;

5) automation of what computers do better than humans: consideration of too many factors and data, complex planning and logical recommendations with OPTIONS for learners to choose from;

6) teachers with means to easily UPDATE and REFOCUS content and ADJUST a system’s teaching style.

I hope this sounds not bad at all. That is what we offer in our Adaptive Learning-Authoring platform CLARITY https://www.itutorsoft.com

So, educators do not need to “rethink” a lot, they just need make a right choice and go ahead.

Thanks

Vlad

 

What Smart Sparrow says:

“We believe adaptive courseware shouldn’t be some sort of magic, super complex, black box algorithm. Instead you have complete control over how your teaching adapts to individual students. You can target what your students are doing at any given point in your lesson and decide on what action to take – either provide feedback or direct them down an adaptive pathway”.

Our Translation:

Sorry, we have no any adaptive, magic, complex courseware. Instead we have you, teachers, who can do this adaptive, magic, super complex job manually by using our ancient branching tool.

Discussion:

The main features of Smart Sparrow presented as innovative ones seem to be pretty ancient:

• “Adaptive Pathways” means just manual branching of Content items using a “Conditions-> Actions” table
• “Adaptive Feedback” is manual connecting student’s possible choices to relevant Feedback Items by using the same “Conditions-> Actions” table
• “Knowledge Analytics” automatically collects learning data and populates them on the teacher’s dashboard. So, teachers can see and decide how to refine their course/lesson
• “Simulations” developed by programmers can be added to courses/lessons. Good Simulations are good by themselves and can add value to any other course not to Smart Sparrow course only.

In Smart Sparrow, teachers have a big deal of freedom to manually program how the course will works with learners, which is good. But it is extremely labor/time-consuming and error-prone, which is bad. Not many teachers have necessary programming skills.

In Knewton platform, the situation is quite opposite. A big part of the course is already pre-programmed for teachers, so it is easy for teachers to complete the course, which is good. But teachers have no freedom to define how the course will works with learners, which is bad.

The best solution is supposed to combine the best features of both platform, exclude bads and be flexible enough to go anywhere in between. It should not ignore the legacy of Instructional System Design and Intelligent Tutoring Systems.

Conclusion:

Smart Sparrow is a low-tech solution, which overloads course/lesson authors with enormous and error-prone programming (bad), but give a few (bad) of programming teachers a freedom to program what they want (good) with questionable quality (bad).

Knewton, in contrast, is a high-tech solution, which automates a course design (good) and takes control away from teachers (bad). Even worse, Knewton’s courses are limited with drill and practice for test passing. It also ignores Instructional System Design principles (bad). See our more detailed analysis.

In comparison with these 2 platforms, CLARITY is that best solution. It visualizes and simplifies authoring by filling in the blanks of the general Activity framework, prevents many errors, verifies and assures quality of courseware. Its Tutoring Engine uses created Courseware and a Personal Dynamically Updated Profile of the learner to automatically plan and recommend the next Content/Test item to the learner. It visualizes a learning progress in detail, transforming a learning into an achievement game, it let a learner drive, …. and does much more to assure learning joy and success.

Moreover, CLARITY is based on strong foundation of general Systems and Activity theories, latest in Instructional System Design and Intelligent Tutoring Systems.

The survey focused on innovative initiatives and strategies for improving three student-centered outcomes: degree completion rates, the quality of student learning, and affordability (i.e., students’ ability to cover the costs of earning a degree).

The main result of the survey:

Respondents view intelligent adaptive learning technologies—instructional software that adjusts the material presented to students in real-time, as students interact with the software—as the most promising initiative for improving the quality of student learning. Systematic assessment of student learning is also viewed favorably by a majority of respondents as an intervention to improve student learning.

Here is more coverage of the survey.

Their Predictions appeared to be in sync with our vision described earlier in our website. It is an honor and pleasure!

“User generated content for learning will grow exponentially. There will be more content generated by users and the internet than what companies can produce on their own; organizations will need to hold learning summits with users to agree on what blend of user and corporate generated content will be the right mix to drive learning; “SMEs will have to yield to Mayors of Four Square” on who has the best knowledge vantage point – the expert or the employee.”

“SMEs will have to yield to Mayors of Four Square”
Learning elections: learners will cast votes for the learning that is best for them and vote up or down on content that is most relevant to them; learning and development will hold elections and have people vote on the best way to develop and deliver content.

Here is one more excellent post by Mark Rosenberg about technologies revolutionizing education.

And the most interesting point is it is not about technologies (motion picture, radio, television, computers, videodiscs, smartboards, smartphones, tablets, MOOCs, …) !

He clarifies: “It is how we use technology to enhance learning, not the technology itself, which counts. What matters much more are learning design, learner preparation, engagement and support, learning facilitation, and yes, in classroom settings, how good the teacher is. This is all about instructional design, pedagogy, learning psychology, effective communications, and organizational culture. It is also about practice and feedback in applying learning to tasks—in work and in life.”

That is right! But what if the technological Tool is specifically designed for improving or better usage of what really matters:

• “learning design, learner preparation, engagement and support, learning facilitation”
• “instructional design, pedagogy, learning psychology, effective communications, and organizational culture”
• “practice and feedback in applying learning to tasks—in work and in life”

Unfortunately, there is no answer. Marc does not consider such technological tools. Meanwhile they exist (Analytical Tools, Authoring Tools, …) and one of them is our online Adaptive Learning-Authoring Platform. Does it mean it can revolutionize education?

Yes, of course, if to use it correctly 🙂