e-Literate

Present is Prologue

Tag: adaptive learning

  • Differentiated, Personalized & Adaptive Learning: some clarity for EDUCAUSE

    Josh Kim wrote three predictions at Inside Higher Ed for the EDUCAUSE 2013 conference, and I particularly agree with the basis of #2:

    Prediction 2: Adaptive Learning Platforms Will Be the Toast of the Party

    Everyone will want to talk to Knewton. The ASU / Pearson / Knewton partnership is a huge deal. Knewton has the technology, relationships, funding, and management team to make a huge impact.

    I’ll be looking at EDUCAUSE at the other adaptive learning players. Where are they focusing their platform work? What deals and relationships do they currently have? How big is their market penetration? What is the quality of their leadership team and employees they have a EDUCAUSE?

    I’m betting we will see at least one major adaptive learning vendor announcement. A purchase, a big collaboration deal, or a new huge round of funding.

    I also expect much of the discussion this year to be on adaptive learning. But one risk of this zeitgeist (if it comes to pass) is that terminology becomes fuzzy and often devoid of meaning. Hey, get your adaptive here. You want to be adaptive, don’t you? We are the adaptive makers… and we are the dreamers of dreams.

    (more…)

  • No, really, courseware is a thing now

    In the operating plan slide deck that Cengage recently released as a consequence of their bankruptcy proceedings, the executive summary slide says that a key element of their strategy is “driving aggressive digital growth in a course model.” “Course solutions” is mentioned three times in the deck as well. Cengage, as a company, is essentially betting its future on courseware. Not just digital products in general, but courseware in particular.

    But they are hardly the only provider building content in this relatively new category. I thought it might be useful to provide a run-down of who is doing what in this space. It turns out that there is a pretty wide range in terms of approaches to the product category.

    (more…)

  • Knewton (Quietly) Pivots

    Knewton CEO Jose Ferriera has an interesting and revealing blog post up about “the coming adaptive world.” In part, it is a response to a report on adaptive learning by Education Growth Advisors. Jose writes, “Despite our constant protestations to the contrary, observers often confuse Knewton with the many adaptive learning app makers who are now popping up. Or they confuse app makers with platforms. Or they think we’re all competitors.” It’s a bit of a red herring, since the report does distinguish between platform and publisher business models. That said, the meaning of the distinction between these two categories isn’t drawn terribly clearly, and it’s fair for Knewton to try to clarify its market positioning. But in doing so, Jose reveals what appears to be a shift in their thinking about the market for a platform like theirs which tells us something important about the ed tech market in general. (more…)

  • A Taxonomy of Adaptive Analytics Strategies

    I almost never quote a blog post in its entirety, but this one from Dan Meyer is so good that I just can’t bear to cut a single word:

    Stephanie Simon, reporting for Reuters on inBloom and SXSWedu:

    Does Johnny have trouble converting decimals to fractions? The database will have recorded that – and may have recorded as well that he finds textbooks boring, adores animation and plays baseball after school. Personalized learning software can use that data to serve up a tailor-made math lesson, perhaps an animated game that uses baseball statistics to teach decimals.

    Three observations:

    One, it shouldn’t cost $100 million to figure out that Johnny thinks textbooks are boring.

    Two, nowhere in this scenario do we find out why Johnny struggles to convert decimals to fractions. A qualified teacher could resolve that issue in a few minutes with a conversation, a few exercises, and a follow-up assessment. The computer, meanwhile, has a red x where the row labeled “Johnny” intersects the column labeled “Converting Decimals to Fractions.” It struggles to capture conceptual nuance.

    Three, “adores” protests a little too much. “Adores” represents the hopes and dreams of the educational technology industry. The purveyors of math educational technology understand that Johnny hates their lecture videos, selected response questions, and behaviorist video games. They hope they can sprinkle some metadata across those experiences — ie. Johnny likes baseball; Johnny adores animation — and transform them.

    But our efforts at personalization in math education have led all of our students to the same buffet line. Every station features the same horrible gruel but at its final station you can select your preferred seasoning for that gruel. Paprika, cumin, whatever, it’s yours. It may be the same gruel for Johnny afterwards, but Johnnyadores paprika.

    Dan captures most of what I was trying to get at with my rant on the big data hype, but much more clearly and succinctly. Points two and three are the most salient here. First of all, the sort of surface-level analysis we can get from applying machine learning techniques to the current data we have from digital education system is insufficient to do some of the most important diagnostic work that real human teachers do. Think about the math classes is which you had to show your work on your homework. Why was that important? Because the teacher needs to see not only what you got wrong but why you got it wrong. Teachers generally don’t just say, “You got three out of five problems involving converting decimals to fractions wrong. Go study some more.” They sit down and work through the problems with the student to find the source of the errors. It’s really hard to get computers to do this well, even with highly procedural domains like math. (Forget about, say, literary analysis.) So in the vast majority of cases, we don’t even try to design systems where students show their work. And without the step-by-step data, no fancy algorithm is going to teach Johnny.

    Second, if the problem is that your content isn’t what the student needs, no fancy algorithm is going to fix that either. Videos are a prime example. I know of one textbook publisher whose teacher customers report that students won’t watch the publishers’ videos, but they can and do find videos on the same topic on YouTube and share them with each other. Think about that. Video-based pedagogical support is valuable enough to the students that they will expend energy searching for videos and sharing them. But they reject the expensive, carefully crafted videos from the publisher that are served up to them on a silver platter. It’s not that the publisher-supplied videos are necessarily “bad” in the sense that they have poor production qualities or are unclear or factually inaccurate. But the students have a particular use in mind for the videos. Maybe they’re struggling with a particular homework problem and just need a quick walk-through of a technique so that they can see the step that they are missing, for example. If the video doesn’t fit their needs—both utilitarian and aesthetic—then it won’t get used. Serving it up adaptively isn’t going to help that problem.

    That said, it’s worth taking a little time to break down the different types of adaptive learning analytics into a couple of categories and see just what we should and should not reasonably hope to gain from them.

    (more…)

  • The Billion-Dollar Bet on an Adaptive Learning Platform

    Update (2/15/13): Per Apollo Group, the billion-dollar investment mentioned in the Chronicle article (quoted below) refers to total infrastructure – not just the learning platform itself. The total infrastructure includes new CRM, portal, and administrative systems.

    *****

    Earlier this week I wrote about the new patent awarded to the University of Phoenix (the for-profit institution owned by the Apollo Group) for the activity stream within their new online learning platform. The patent gives us a glimpse into a billion-dollar bet that Phoenix is making on this next-generation LMS that will power their move into adaptive learning.

    The University of Phoenix has always been known for using a homegrown LMS, which is understandable given the large size (360,000 students) of the school. In 2009, Phoenix began investing in a completely new learning platform as part of the “Learning Genome Project”. While the company has traditionally been reluctant to describe its internal systems, starting with the 2010 EDUCAUSE conference Phoenix began sharing more information on this project.

    The promise of adaptive learning

    Steve Kolowich at Inside Higher Ed wrote an article on the new learning platform in October 2010 based on information shared by Phoenix’s Director of Data Innovation.

    (more…)