Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning to engage students with the subject and improve their understanding of the material. We describe the use of a fuzzy linguistic summarisation (LS) technique for extracting linguistically interpretable rules from student data describing prominent relationships between activity/engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem-based learning tasks. The proposed system is developed as a set of services to cater for data heterogeneity and deployable on a cloud computing platform. We present a case study and experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a group performance model (GPM) to deploy activity led learning (ALL) in a master-level module. Results show that the fuzzy rules can identify useful relationships between student engagement and performance.
|Journal||International Journal of Communication Networks and Distributed Systems|
|Publication status||Published - 2014|
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- activity led learning
- fuzzy systems
- linguistic summarisation
- student performance monitoring