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@inproceedings{1392193,
author = {Pelánek, Radek and Řihák, Jiří},
address = {New York, NY, USA},
booktitle = {Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization},
doi = {http://dx.doi.org/10.1145/3079628.3079667},
keywords = {mastery learning; learner modeling; Bayesian knowledge tracing; exponential moving average},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-4635-1},
pages = {156-163},
publisher = {ACM},
title = {Experimental Analysis of Mastery Learning Criteria},
url = {https://dl.acm.org/citation.cfm?id=3079667},
year = {2017}
}
TY - JOUR
ID - 1392193
AU - Pelánek, Radek - Řihák, Jiří
PY - 2017
TI - Experimental Analysis of Mastery Learning Criteria
PB - ACM
CY - New York, NY, USA
SN - 9781450346351
KW - mastery learning
KW - learner modeling
KW - Bayesian knowledge tracing
KW - exponential moving average
UR - https://dl.acm.org/citation.cfm?id=3079667
N2 - 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.
ER -
PELÁNEK, Radek and Jiří ŘIHÁK. Experimental Analysis of Mastery Learning Criteria. Online. In \textit{Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization}. New York, NY, USA: ACM, 2017, p.~156-163. ISBN~978-1-4503-4635-1. Available from: https://dx.doi.org/10.1145/3079628.3079667.
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