A key challenge of online learning is keeping students engaged and motivated, and reacting quickly if a student is ‘at-risk’ of not completing their qualification or falling behind in their studies.
Until 2014, Advisers manually tracked student engagement, such as time spent on our learning platform, assignment submissions and discussion forum contributions. At-risk students were then identified from the data collected and contacted individually by telephone or email. As our student population increased, this process became more and more labour intensive and resulted in too much time being spent analysing data instead of supporting students.
This poster presentation demonstrates how KOL developed a state-of-the-art Learning Analytics System (LAS) to automate this process, with compelling results. The LAS utilises live data from our learning platform, identifies at-risk students and automatically sends out customised, personalised emails to students. Emails are automatically sent from each student’s personal Student Adviser and contain course and student specific reminders and deadlines. To prevent the possibility of sending students too many automated emails, the LAS limits the number of emails sent to each student per module. Student Advisers also have the flexibility to deactivate and adjust the type and frequency of emails in accordance with student preferences. The LAS also provides Advisers with a dashboard view of each student’s engagement with the VLE allowing them to easily identify at-risk students and tailor their level of support and communication.
Since implementation, a manual process that was taking hours each day can now be completed in seconds at the click of a button. This has enabled the Student Advisers to spend time focussing on at-risk students who require additional, individual support.
A number of key indicators were identified to help conduct an impact analysis of this initiative. These indicators included measurements of student satisfaction and various measures of student performance. One measure, our 2015 NSS student satisfaction rating soared to 95%, whilst we also recorded improvements in student performance in areas such as module completion rates, speed of study and the number of students actively studying with us.
The poster presentation will show how this innovative use of learning analytics can be used to enhance the student experience and engagement, streamline administrative processes and improve student retention. It also provides an analytical insight into online learning behaviour and demonstrates how support can be tailored to individual needs at scale.
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