| PELÁNEK, Radek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Online. In Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay. Artificial Intelligence in Education. New York: Springer, 2018, p. 450-461. ISBN 978-3-319-93842-4. Available from: https://dx.doi.org/10.1007/978-3-319-93843-1_33. |
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@inproceedings{1448696,
author = {Pelánek, Radek},
address = {New York},
booktitle = {Artificial Intelligence in Education},
doi = {http://dx.doi.org/10.1007/978-3-319-93843-1_33},
editor = {Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay},
keywords = {mastery learning; student modeling},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York},
isbn = {978-3-319-93842-4},
pages = {450-461},
publisher = {Springer},
title = {Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge},
year = {2018}
}
TY - JOUR
ID - 1448696
AU - Pelánek, Radek
PY - 2018
TI - Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge
PB - Springer
CY - New York
SN - 9783319938424
KW - mastery learning
KW - student modeling
N2 - Mastery learning is a common personalization strategy in adaptive educational systems. A mastery criterion decides whether a learner should continue practice of a current topic or move to a more advanced topic. This decision is typically done based on comparison with a mastery threshold. We argue that the commonly used mastery criteria combine two different aspects of knowledge estimate in the comparison to this threshold: the degree of achieved knowledge and the uncertainty of the estimate. We propose a novel learner model that provides conceptually clear treatment of these two aspects. The model is a generalization of the commonly used Bayesian knowledge tracing and logistic models and thus also provides insight into the relationship of these two types of learner models. We compare the proposed mastery criterion to commonly used criteria and discuss consequences for practical development of educational systems.
ER -
PELÁNEK, Radek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Online. In Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay. \textit{Artificial Intelligence in Education}. New York: Springer, 2018, p.~450-461. ISBN~978-3-319-93842-4. Available from: https://dx.doi.org/10.1007/978-3-319-93843-1\_{}33.
|
