Tag Archives: student data

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

This paper focuses on work conducted at The Open University (OU), one of the world’s largest distance learning institutions, into predicting students who are at risk of failing a module. Since tutors at the university do not interact face to face with students, it can be difficult for to identify and respond to students who are struggling in time to resolve the difficulty. Predictive models were developed and tested using historic Virtual Learning Environment (VLE) activity data, combined with other data sources, for three OU modules. These revealed that it is possible to predict student failure by looking for changes in user activity in the VLE. More focused analysis of these modules yielded some promising results for the creation of accurate hypotheses about students who fail.

Comment by Martyn Cooper: This paper reports an investigation in online tutoring of the relation between learning analytic metrics and retention. It highlights the important result that little can be deduced from absolute levels of interaction but changes in level of interaction are significant. Although a limited study in the context of one VLE and a few modules it is likely to be a paper with wider significance.

Citation: A. Wolff, Z. Zdrahal, A. Nikolov and M. Pantucek, (2013). Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment, In: Third International Learning Analytics and Knowledge Conference (LAK13), 8-12 April, Leuven, Belgium. | Url: http://dl.acm.org/citation.cfm?id=2460296