Author: Vladimir Goodkovsky

“It is our children’s creativity that is most valuable. 60% of today’s jobs will not be needed in the near future, and 65% of today’s grade school kids will end up at jobs that haven’t been invented yet (US Department of Labor, Future work). The old jobs are being replaced by automation, algorithms and robots, however, the jobs not being replaced are those that computers are not good at, the ones that require human creativity, passion and curiosity to figure out novel solutions. Unfortunately, the traditional educational model, with its standardization and broadly issued textbooks is teaching the exact opposite, which was fine for the first 100 years of its operation, but now it’s obsolete. Our outdated and slow to move educational systems are not adapting quickly enough to empower our children in this new world, especially those most under-served children in poverty and areas of inadequate support. Our children are not being adequately prepared for the world they will be growing up in. We only need to look at the number of graduates not able to find work -even those apparently well educated- to see the beginnings of this (53.6% of bachelor’s degree-holders under the age of 25 are jobless or underemployed: US department of Labor).  We need an educational model that, from an early age, fosters their curiosity, their creativity, their entrepreneurial skills and passions. An educational model that operates like the real world and teaches through doing, through sharing and through exploring, an educational model where everyone is both a learner and a teacher and learning is a continual, lifelong and empowering process.”

See a full text here.

There are those who think that the old ways of training are still valid and sufficient for today’s workforce, and there are those who realize that the world has moved on and a new approach to supporting workplace learning is essential.

We all know the old stuff the Traditionalists prefer. Let us see what the second group of L&D professionals, Modern Workplace Learning (MWL) practitioners, do:

• They are rejecting the creation of expensive, sophisticated e-learning content and preferring to build short, flexible, modern resources (where required) that people can access when they need them. AND they are also encouraging social content (or employee-generated content) – particularly social video – because they know that people know best what works for them.
• They are ditching their LMS (or perhaps just hanging on to it to manage some regulatory training) – because they recognize it is a white elephant – and it doesn’t help them understand the only valid indicator of learning success, how performance has changed and improved.
• They are moving to a performance-driven world – helping groups find their own solutions to problems – ones that they really need, will value, and actually use, and recognise that these solutions are often ones they organise and manage themselves.
• They are working with managers to help them develop their people on the ground – and see the success of these initiatives in terms of impact on job performance.
• They are helping individuals take responsibility for their own learning and personal development – so that they continuously grow and improve, and hence become valuable employees in the workplace
• They are supporting teams as they work together using enterprise social platforms – in order to underpin the natural sharing within the group, and improve team learning.

Full text of this post by Jane Hart is available here.

“Maybe, in the end, the purpose of school is to help our kids find their own sense of purpose. To prepare them for a life where they can set, and achieve, their own goals, not grind away to meet the needs of some bureaucrat or college admissions officer.”

Here is the full text of the paper with a lot of interesting comments.

Actually, this is the ultimate purpose of our platform: to help learners to learn, perform, plan and design different (societal, organizational, professional, learning) activities in their purposeful connection by themselves.

The New York Times columnist David Brooks wrote about the Sundance-selection “Most Likely To Succeed” film, positing that the ultimate goal of school is to erect “cathedrals of knowledge and wisdom . . . based on the foundations of factual acquisition and cultural literacy.” He probably meant the same.

 

Using “big data” to help match people to courses could cut freedom of choice and ultimately put students off higher education, an expert has warned. Read more

“Over the past 20 years of my career in learning reform, I feel like the overarching education community has consistently communicated the same two messages: 1. The system isn’t working and 2. Everything has to stay the same for it to work. Hmmm. This presents a particular challenge for organizations seeking to partner with schools, because it effectively requires us to create what I am going to call “palatable disruption.” That is a product or service that can effectively transform without unsettling or creating too much friction in the system. Palatable disruption is particularly critical when you take into consideration the findings from EIA and Digital Promise’s Ed-Tech Purchasing report, that education leaders decide what to buy primarily based on referrals. That means your education partners need to enjoy your particular brand of disruption even before they see the outcomes so they can recommend it to their peers. That is the key to quickly ‘cresting the wave of early adopters’¹  and breaching the mass market. Not always so easy if your goal, as is ours, is to truly move the needle in teaching and learning.”

This is a very informative quote from the article describing the reality of the Education Transformation.

Click here to read the whole article.

DreamBox Claims

DreamBox captures every decision a student makes and adjusts the student’s learning Path both within lessons and between lessons, thereby providing millions of individualized learning paths, each tailored to a student’s unique needs in real-time. It represents a systemic way for students to master skills and knowledge levels at a pace that is especially tailored to their strengths and weaknesses. It provides unprecedented visibility into data on student achievement to inform teachers’ daily practice.

DreamBox vs CLARITY Critique

1) Content structuring and granularity is limited, just a set of disconnected lessons. (No Architecture of Content causes No Architecture (chaos) of learners’ experience). The lessons don’t even have explicit Objectives (which is a fundamental concept of instructional design), just a script (single Path) hidden behind the scenes.

In contrast, our CLARITY platform supports an explicit multilevel structure of Content. Each Lesson has explicit Objectives and Tasks aligned to Objectives, which is critically important to assure high quality of teaching and learning.

2) In practice, DreamBox recommends just a “right” Next Lesson. This approach can be qualified as Macro/Coarse-Management without any supervising within the lesson. What learners need most is a Micro/Fine-Management within a Lesson.

In contrast, CLARITY makes recommendations on any level of Content Granularity including Course, Chapter, Section, Lesson as well as Tasks within each Lesson. It also generates multiple recommendations, not just one assignment, to facilitate learner’s own choice.

3) DreamBox, like many other adaptive learning systems, uses pretest to identify knowledge/skills gaps. These gaps are then used to prescribe one of predefined personal Paths. Despite the claim that DreamBox uses Big Data and may generate millions of Path, it misses the point. It prescribes an average Path of similar learners, not what individual learner really wants to know right now when she needs it for her success. So, each learner is supposed to passively follow the prescribed static Path.

In contrast, CLARITY does not create a whole individual Path in advance. It dynamically generates next step recommendation(s) and a learner may make her own choices at every step of the educational process. Our platform helps a Learner in real-time to dynamically create her own learning path based on her own wishes, needs and preferences, not on average needs and preferences of others).

4) What DreamBox lacks is the dynamically generated human tutor-like interactivity, which is proven to be the most effective. Dynamic generation is incomparably richer than any single Path, and may provide branching, loops of repetitions, root-cause diagnosing, rolling back and exactly focused remediation starting from roots. Such rich interactivity is a condition of personal success in learning. Absence of such rich interactivity in DreamBox’ individual Paths creates multiple dead-ends in individual learner’s progress.

In contrast, our CLARITY platform dynamically generates all of these branching, loops of repetitions, root-cause diagnosing, rolling back and exactly focused remediation starting from roots. As a result, it does not create dead-ends in learning progress and guarantees individual success of practically all students.

5) DreamBox lacks a Learner Model, which is a MUST part of any Intelligent Tutoring System. That is why DreamBox is not really Intelligent and cannot claim effectiveness of Intelligent Tutoring Systems.

In contrast, our CLARITY platform includes an explicit Learner Model, which is visually presented to Teachers and Learners. Our Intelligent Tutoring Engine has all necessary components of classical Intelligent Tutoring Systems and theoretically is the most cost-effective Intelligent Tutoring System Platform on the market now.

6) DreamBox has no its own Authoring Tool. But the Authoring Tool is a critically important instrument that allows the authors to effectively create new content without any programming and teachers to update, adjust and improve content.

In contrast to DreamBox, our easy-to-use web-based Authoring Tool opens new horizons for authors and teachers to collaborate in creating, updating, adjusting, and perfecting content.

7) DreamBox has no explicit unified framework for content representation and development. Each lesson is developed as a unique monolith piece of software with participation of programmers. It is a very labor intensive method.

In contrast, our CLARITY lesson is based on explicit unified framework for content representation, authoring and sequencing. Namely, this unified content framework has facilitated creation of our unified Learner Model, unified Tutoring Engine and unified Authoring Tool. Altogether these components of software significantly simplify the process of creating AI-based courses of any complexity in any domain.

8) DreamBox as it is now has no future because of its limited technology, not unified content, absence of the learner model and extremely inefficient production of content.

In contrast, our CLARITY platform is a future of personally supervised learning (tutoring), education and training due to:

(a) Unification of content and learner representation in any domain

(b) Complete automation of full-fledged Intelligent Tutoring functionality

(c) Recommendation Engine providing rich supervising functionality to guarantee learning success

(d) Unified Authoring Tool for rapid-to-serious creation/perfection of any specific content

(e) Unlimited capacity for further improvement.

Between us, our Adaptive Learning-Authoring Platform is the next big thing happening in Education. READ MORE

“After seven years, $100 million, and the dedication of hundreds of Knewton employees, we’re proud to unveil Knewton.com — the world’s first and only open adaptive learning platform. We think of it as a genius robot tutor in the cloud. It takes the best traits of a human tutor and makes it free, unlimited, on-demand, personalized and available to the world.”

That is the introduction of a new beta version of Adaptive Learning Platform by Jose Ferreira.

With all due respect, let us take a critical look at what Knewton really offers:

1. Authors cannot create a new course, category, topic, subtopic, … of content. Everything is already predefined by Knewton from a pre-published source of content (like a textbook or taskbook)
2. Authors can select topics, but they are not masters of content anymore, they are rather slaves of Knewton’s automation
3. There is no way to define learning Goal/Objectives by Authors. These fundamental instructional concepts do not exist in Knewton’s vocabulary. Authors are only allowed to add and specify presenting and testing items, but not target nor connect them
4. The assignment (for clarity I would name it a lesson) content structure is completely hidden from authors (authors are more like constructors, not architects. No room for human architects of content anymore, they are replaced with robots)
5. Assignment (lesson) content is amorphous, no explicit structure, no hierarchy, no generalization, no holistic view.
6. Content interconnections are defined behind the scenes automatically with a kind of keywords-based distance metrics.
7. No content quality assurance, which translates to No learning quality assurance
8. No root-cause diagnosing, authors can define only shallow feedback on incorrect answers. It can cause dead-loops in learning.9. In general, Knewton keeps the teachers/educators out of the course creation process. Instead, it “creates” drill & practice courses automatically from the pre-published textual material.

1. Learners can select topics of study, but can do nothing about how to learn it.
2. The content organization and logic of learning is completely hidden and is not clear for learners. So it is a kind of a blind drill without a purpose in mind.
3. Assignments (“Your” and “Suggested” ) are very coarse (course or lesson level), which means coarse, not precisely focused, supervising of learning process
4. Objectives, Purposes of learning are not communicated to, and unknown by, learners. For learners, learning is just a Path to nowhere
5. The progress of learning is represented with a moving (forth and back) bar. So, probably the Learning Objective is to hit the predefined threshold on that bar, but it is not specified enough to be meaningful.
6. It does not teach learners how to learn better on your own
7. No self-driven learning on your own, there is no room for that luxury
8. It is a drill & practice robot for passing tests on low-level robotic kind of skills. The human high-level analysis and creativity skills are out of consideration at all.

1. Teachers are not helped much. The progress of learning represented by moving bar is too superficial for teachers. The bar (dynamic score) is not informative enough for making constructive teaching decisions. Analytics is beautiful but shallow. So, the technology does not really help teacher to make or recommend meaningful decisions, teachers are supposed to do some extra manual research on their own to make them
2. The technology does not improve existing teaching towards “an ideal teaching model”, because it is based on the same traditional teachers’ manual decision-making supported with extra but shallow analytic data.
3. No trace of ANY pedagogy behind!? It means no ground for implementation
4. It is not even a competency-based education, because there is no room for competencies to define

In general, it is not a proper educational technology, it is a nice technology but designed without proper pedagogical foundation, vision and goal.

May be that is why David Kuntz, chief research officer of Knewton, said that “the company regularly faces resistance from professors who try the approach. … [This] instruction is not the way they’ve always done things.”

Take also a look at “Yes, I did say that Knewton is “selling snake oil” by Michael Feldstein.

Fortunately, our Adaptive Learning-Authoring Platform CLARITY is quite different. It clarifies and fixes all the above deficiencies and designed for humans. READ MORE.