Activity sequence modelling and dynamic clustering

Authors: Mirjam Köck Alexandros Paramythis

Publish Date: 2011/01/11

Volume: 21, Issue: 1-2, Pages: 51-97

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Abstract

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments We present an approach based on the modeling of learners’ problem solving activity sequences and on the use of the models in targeted and ultimately automated clustering resulting in the discovery of new semantically meaningful information about the learners The approach is applicable at different levels to detect predefined wellestablished problem solving styles to identify problem solving styles by analyzing learner behaviour along known learning dimensions and to semiautomatically discover learning dimensions and concrete problem solving patterns This article describes the approach itself demonstrates the feasibility of applying it on realworld data and discusses aspects of the approach that can be adjusted for different learning contexts Finally we address the incorporation of the proposed approach in the adaptation cycle from data acquisition to adaptive system interventions in the interaction process

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