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.