article

Activity sequence modelling and dynamic clustering for personalized e-learning

Authors:

Alexandros ParamythisAuthors Info & Claims

User Modeling and User-Adapted Interaction, Volume 21, Issue 1-2

Pages 51 - 97

https://doi.org/10.1007/s11257-010-9087-z

Published: 01 April 2011 Publication History

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 pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world 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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Maqsood RCeravolo PRomero CVentura S(2022)Modeling and predicting students’ engagement behaviors using mixture Markov modelsKnowledge and Information Systems10.1007/s10115-022-01674-964:5(1349-1384)Online publication date: 1-May-2022
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Activity sequence modelling and dynamic clustering for personalized e-learning
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cover image User Modeling and User-Adapted Interaction

User Modeling and User-Adapted Interaction Volume 21, Issue 1-2

April 2011

244 pages

ISSN:0924-1868

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Copyright © Copyright © 2011 Springer Science+Business Media B.V.

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Published: 01 April 2011

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https://dl.acm.org/doi/10.1007/s10462-023-10508-1
Maqsood RCeravolo PRomero CVentura S(2022)Modeling and predicting students’ engagement behaviors using mixture Markov modelsKnowledge and Information Systems10.1007/s10115-022-01674-964:5(1349-1384)Online publication date: 1-May-2022
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