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Present is Prologue

Tag: Retention Center

  • Blackboard Analytics Update

    In my last post, I promised that I would give an update specifically on the state of Blackboard’s learning analytics. Well, here you go. This is a summary of what I learned about their product from a chat with Mark Max, Blackboard’s VP of Learning Analytics and, to a lesser degree, with VP of User Experience Stephanie Weeks. I wrote about Blackboard’s Retention Center product some time ago. That product (or feature set, since it is free in Blackboard) directly competes with Desire2Learn’s Student Success System. This post is more broadly about their Analytics product suite, which is most directly analogous with Desire2Learn’s Insights product, although it is actually much, much broader in scope.

    The short version is this: Blackboard has very solid and reliable technology base from which they are building their learning analytics. It is easily the most mature platform among the LMS providers from that perspective. What they are a little short on is vision. In other words, they are pretty much the mirror image of Desire2Learn.

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  • Desire2Learn’s Analytics Product Looks Very Good

    I’ve been super-busy with a consulting gig over the last few weeks and have fallen off the wagon with my blogging. This is one of the many reasons that I am grateful to have Phil as my prolific yet profound co-publisher and that we have attracted a group of terrific featured bloggers.

    Anyway, I thought I would get back to business with a long-overdue post about D2L’s learning analytics product, called “Insights.” There are several pieces to the product, but I’m going to focus on the component that they call the Student Success System. I have blogged from time to time on Purdue’s Course Signals project, (now commercialized in an offering from Elucian), as having set the bar for student retention analytics. More recently, I wrote about Blackboard’s Retention Center, which is clearly following in Purdue’s footsteps. My impression of Retention Center is that it is a reasonable Version 1 product that captures some but not all of the value of Course Signals.

    D2L’s Student Success System also follows in Purdue’s footsteps. But rather than simply playing catch-up, I would call it an incremental but meaningful improvement over Course Signals in most aspects. From what I can tell based on initial conversations with D2L about the product details, this now appears to be the system to beat. (I reserve the right to change my opinion based on implementation experiences from clients, which are particularly important for this product.)

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  • Blackboard’s New Early Warning Analytics Product

    A couple of weeks ago in my post about the different types of learning analytics, I described retention early warning systems thusly:

    Most people don’t think about early warning systems as being in the same category as adaptive analytics, but if you consider that “adaptive” really just means “adjusting to your personal needs,” then a system like Purdue’s Course Signals is, in fact, adaptive. It sees when a student is in danger of failing or dropping out and sends increasingly urgent and specific suggestions to that student. It does that without “knowing” anything about the content that the student is learning. Rather, it’s looking at things like recency of course login (Are you showing up for class?), discussion board posts (Are you participating in class?), on-time assignment delivery (Are you turning in your work?), and grade book scores (Is your work passing?), as well as longitudinal information that might indicate whether a student is at-risk coming into the class. What Purdue has found is that such a system can teach students metacognitive awareness of their progress and productive help-seeking behavior. It won’t help them learn the content better, but it will help them develop better learning skills.

    Well, last week, Ray Henderson announced Blackboard’s new Retention Center and described it as follows:

    The Retention Center gives critical insight on learning and activity gaps to instructors, within the LMS, that helps them quickly diagnose students that are falling behind. Pre-configured and automatic so they don’t have to hunt for it. No set-up: it automatically calls out students that are at risk while instructors still have time and space to do something about it. With the feature instructors can see:

    • Who’s logging in: this is a simple but powerful predictor of student success. Instructors see how long it’s been since students have logged in to the course and how many have been away for five days or more. And not by fishing through student profiles or reports but in an automatic view complete with red flags where they’re needed.
    • Whether they’re engaged: which students have had low levels of course activity, at 20 percent or below the average in the last week.
    • Whose grades are suffering: which students are currently trending at 25 percent or more below the course average so they can target extra help to where it’s most needed – even when it isn’t asked for.
    • Who has missed deadlines: instructors might know this anecdotally or on a case-by-case basis, but now they can get a real-time view of all students that have missed one or more deadline.

    Eerily similar, no? A number of years back, when I pressed Course Signals inventor John Campbell on which factors in the LMS are most highly predictive of student success across different courses, he named exactly these four. The only surprise here is that this isn’t a common analytics feature of every LMS and courseware platform on the market yet. Purdue proved that their value in helping at-risk students is high. I’m glad Blackboard is stepping up.

    The one piece that’s missing is a simple standard where an SIS or other longitudinal data system could pass an at-risk “credit score” to the early warning system to modify its sensitivity. If a student on the honor roll drops off the radar for a week, it’s less of a cause for concern that a student on academic probation (for example). I tried to push this idea for a standard at the IMS a few years back but got nowhere with it at the time. I hope that Blackboard will push for something like it now that they have a system to take the data.