Další formáty:
BibTeX
LaTeX
RIS
@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 a 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, s.~156-163. ISBN~978-1-4503-4635-1. Dostupné z: https://dx.doi.org/10.1145/3079628.3079667.
|