David Wiley has a really interesting post up about Lumen Learning’s new personalized learning platform. Here’s an excerpt:
A typical high-level approach to personalization might include:
- building up an internal model of what a student knows and can do,
- algorithmically interrogating that model, and
- providing the learner with a unique set of learning experiences based on the system’s analysis of the student model
Our thinking about personalization started here. But as we spoke to faculty and students, and pondered what we heard from them and what we have read in the literature, we began to see several problems with this approach. One in particular stood out:
There is no active role for the learner in this “personalized” experience. These systems reduce all the richness and complexity of deciding what a learner should be doing to – sometimes literally – a “Next” button. As these systems painstakingly work to learn how each student learns, the individual students lose out on the opportunity to learn this for themselves. Continued use of a system like this seems likely to create dependency in learners, as they stop stretching their metacognitive muscles and defer all decisions about what, when, and how long to study to The Machine.
Instructure’s Jared Stein really likes Lumen’s approach, writing,
So much work in predictive analytics and adaptive learning seeks to relieve people from the time-consuming work of individual diagnosis and remediation — that’s a two-edged sword: Using technology to increase efficiency can too easily sacrifice humanness — if you’re not deliberate in the design and usage of the technology. This topic came up quickly amongst the #DigPedNetwork group when Jim Groom and I chatted about closed/open learning environments earlier this month, suggesting that we haven’t fully explored this dilemma as educators or educational technologist.
I would add that I have seen very little evidence that either instructors or students place a high value on the adaptivity of these products. Phil and I have talked to a wide range of folks using these products, both in our work on the e-Literate TV case studies and in our general work as analysts. There is a lot of interest in the kind of meta-cognitive dashboarding that David is describing. There is little interest in, and in some cases active hostility toward, adaptivity. For example, Essex County College is using McGraw Hill’s ALEKS, which has one of the more sophisticated adaptive learning approaches on the market. But when we talked to faculty and staff there, the aspects of the program that they highlighted as most useful were a lot more mundane, e.g.,
It’s important for students to spend the time, right? I mean learning takes time, and it’s hard work. Asking students to keep time diaries is a very difficult ask, but when they’re working in an online platform, the platform keeps track of their time. So, on the first class day of the week, that’s goal-setting day. How many hours are you going to spend working on your math? How many topics are you planning to master? How many classes are you not going to be absent from?
I mean these are pretty simple goals, and then we give them a couple goals that they can just write whatever they feel like. And I’ve had students write, “I want to come to class with more energy,” and other such goals. And then, because we’ve got technology as our content delivery system, at the end of the week I can tell them, in a very efficient fashion that doesn’t take up a lot of my time, “You met your time goal, you met your topic goal,” or, “You approached it,” or, “You didn’t.”
So one of the most valuable functions of this system in this context is to reflect back to the students what they have done in terms that make sense to them and are relevant to the students’ self-selected learning goals. The measures are fairly crude—time on task, number of topics covered, and so on—and there is no adaptivity necessary at all.
But I also think that David’s post hints at some of the complexity of the design challenges with these products.