Visualisation of Learning Management System Usage for Detecting Student Behaviour Patterns

Haig, T., Falker, K. and Falkner, N.

Identifying \"at-risk\" students - those that are in danger of failing or not completing a course - is a crucial element in enabling students to achieve their full potential. However, with large class sizes and growing academic workloads, it is becoming increasingly difficult to identify students who require urgent and timely assistance. Efficient and easy to use tools are needed to assist academics in locating these students at early stages within their courses. A significant body of work exists in the use of student activity data, e.g. attendance, performance, participation in face-to-face and online sessions, to predict overall student performance and at-risk status. This is often built upon the considerable amount of student data within learning management systems. Manual data collection, including surveys and observation, which introduces additional workload is often required to extract relevant data meaning that in large classes it is prohibitively difficult to apply such techniques. In this paper, we introduce a framework for at-risk identification combining simple metrics, gathered from social network and statistical analysis domains, that have been shown to correlate with student performance and require slow amounts of manual data collection or additional expert analysis. We describe each of the metrics within our framework and demonstrate their usage. We use visualisation to enable easy interpretation of results. The application of our framework is demonstrated within the context of an advanced undergraduate computer science course.

Cite as: Haig, T., Falker, K. and Falkner, N. (2013). Visualisation of Learning Management System Usage for Detecting Student Behaviour Patterns. In Proc. Fifteenth Australasian Computing Education Conference (ACE 2013) Adelaide, Australia. CRPIT, 136. Angela Carbone and Jacqueline Whalley Eds., ACS. 107-115

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