Experimental Analysis of Mastery Learning Criteria
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization |
| MU Faculty or unit | |
| 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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