Using Student Data to Predict Behaviour

  • 3 Min Read

Early intervention can address issues quickly, leading to better outcomes for students.

So much institutional and student success depends on being able to identify early, and predict an “at risk” student. Without the use of a virtual learning environment (VLE), traditional methods of analysing student data and red-flagging possible at risk students relied upon a fairly narrow analysis of a student’s history, such as:

  • Those who have taken a gap year
  • Full-time employed
  • Part-time enrolled

But these only look at the high-level picture and not at the individual work that each student is doing in a course. Mature and returning students comprise a large portion of the online learning enrolment, which means that more attention is required to look beyond the overall data and into their level of course engagement, starting from day one of the course. And this is where a VLE with predictive analytics becomes a key component for analysing data, on a daily basis.

Making big data sets actionable for educators

Educators and advisers can analyse student engagement in order to notice trends that can be quickly acted upon. Using an VLE, they can make automatic changes to a student’s course based on data analysis, which in turn can be used to personalise learning in a more efficient and reliable way. Using class progress tools, educators can link directly to automated emails, triggered by pre-determined behaviours, or grade thresholds. These personalised emails can provide feedback and access to resources that students may find relevant and useful in their studies. But this early intervention also lets students know that they have the support of their educators and advisers, ensuring that they don’t feel disconnected from the institution.

Student data: differential data makes a difference

One of the problems that institutions have when it comes to data management is the sheer volume of information they have to ingest and aggregate. Because of this, drawing reports that promote quick action can sometimes be slow and tedious. Spreadsheets filled with numbers need to be re-organised and interpreted by a team of analysts. In many cases, this must be done manually. Using a VLE that allows consuming incremental changes to the data on a daily basis means that analysts can quickly ingest and process only the data that has changed, rather than having to load a full set of data each time. This helps to identify daily trends at a glance, which means that impactful actions can be taken with speed.

Predicting success in the first three weeks

By noting the level of student engagement through the VLE, advisers and educators have quick access a student’s progress. Interactions such as the completion of quizzes or surveys done on time, or number of posts in a community forum. The amount of time a student spends on assignments or course material, interactions with professors, are indicators of a student’s engagement level.

Georgia Southern University in the US serves as a great example of how an institution has seen success with their learner data to improve retention and graduation rates. Check out their video discussion, presented by Dr. Steven Burrell, VP of IT & CIO. In it, you’ll see how student data has allowed GSU to predict student outcomes to within a letter grade, an amazing 67% of the time.

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