Experimental Analysis of Mastery Learning Criteria

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Authors

PELÁNEK Radek ŘIHÁK Jiří

Year of publication 2017
Type Article in Proceedings
Conference Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
MU Faculty or unit

Faculty of Informatics

Citation
Web https://dl.acm.org/citation.cfm?id=3079667
Doi http://dx.doi.org/10.1145/3079628.3079667
Field Informatics
Keywords mastery learning; learner modeling; Bayesian knowledge tracing; exponential moving average
Description A common personalization approach in educational systems is mastery learning. A key step in this approach is a criterion that determines whether a learner has achieved mastery. We thoroughly analyze several mastery criteria for the basic case of a single well-specified knowledge component. For the analysis we use experiments with both simulated and real data. The results show that the choice of data sources used for mastery decision and setting of thresholds are more important than the choice of a learner modeling technique. We argue that a simple exponential moving average method is a suitable technique for mastery criterion and propose techniques for the choice of a mastery threshold.
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