One of the benefits of online learning programs is that there are tons of digital footprints that you can track to know whether your students are staying on top of things or falling behind.
But with so much activity across so many different tools, how can you know which digital footprints to track? And what’s more - how do you interpret those footprints to know who is keeping up and who is at risk of dropout?
We share the answers to these questions below.
The digital footprints from online learning programs typically fall into one of three essential categories for tracking student engagement. We refer to them as our ABC’s:
Are students showing up to class? Are they on time or are they late? Are they leaving early? Are they engaging during live sessions - participating in conversations, chat, polls? Is their camera on?
Metrics to track:
Are students interacting with their peers on your community platform or LMS? Are they logging into your LMS? Are they taking advantage of office hours or mentor sessions?
Metrics to track:
Are students completing modules on time (or at all)? Are they submitting required assignments?
Metrics to track:
Learn the only two questions you need to ask students to know if they'll succeed here.
Tracking this data is only the first step. The true question to determine whether your students are struggling is: What does this data suggest?
Here are two things you can do to help make sense of your student data to see warning signs as early as possible:
When you view student data over the course of an entire program, you might get some false signals. A student that has missed 3 classes over the course of your whole program might still be on a track, but a student that has missed 3 classes in a 2 week period is one you’ll want to check up on.
This same thinking goes for community engagement. Let’s say a student is extremely active one month and then absent the next two. While the average of this data might look okay, viewing this data by month can give you an entirely different picture.
Viewing a single metric will never give you the full picture of how a student is doing. Maybe the student is fully up to speed, but just doesn’t feel the need to be chatty in your community platform. Maybe a student skipped a few classes because they had taken a previous course or had already read up on the topic covered.
That’s why combining metrics can provide essential clues to how a student is truly faring. If a learner is just missing class, maybe they’re okay. But if they’re missing class, not participating on your community platform, and behind on assignments - those are some significant red flags.
Once you’ve determined who is at risk, that’s when it’s time to act. Learn more about how active intervention can help prevent churn here.
The Virtually Student Relationship Manager (SRM) offers a fully automated option to track, aggregate, interpret, and act on your student data to make sure your students stay on track.
The SRM automates data collection and aggregation from across all of your different tools - attendance, feedback, community engagement, etc. - flags at risk students based on custom triggers you create according to your program’s known warning signs, and automatically reaches out to flagged learners to check in and offer support.
Curious to learn more? Schedule a demo here.
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Laura Marks is Head of Customer Experience at Virtually