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@inproceedings{1607941,
author = {Effenberger, Tomáš and Pelánek, Radek and Čechák, Jaroslav},
address = {New York, NY, USA},
booktitle = {Proceedings of the 10th International Conference on Learning Analytics and Knowledge},
doi = {http://dx.doi.org/10.1145/3375462.3375491},
keywords = {student modeling; learning curves; knowledge components; introductory programming},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York, NY, USA},
isbn = {978-1-4503-7712-6},
pages = {472-479},
publisher = {Association for Computing Machinery},
title = {Exploration of the Robustness and Generalizability of the Additive Factors Model},
url = {https://doi.org/10.1145/3375462.3375491},
year = {2020}
}
TY - JOUR
ID - 1607941
AU - Effenberger, Tomáš - Pelánek, Radek - Čechák, Jaroslav
PY - 2020
TI - Exploration of the Robustness and Generalizability of the Additive Factors Model
PB - Association for Computing Machinery
CY - New York, NY, USA
SN - 9781450377126
KW - student modeling
KW - learning curves
KW - knowledge components
KW - introductory programming
UR - https://doi.org/10.1145/3375462.3375491
L2 - https://doi.org/10.1145/3375462.3375491
N2 - Additive Factors Model is a widely used student model, which is primarily used for refining knowledge component models (Q-matrices). We explore the robustness and generalizability of the model. We explicitly formulate simplifying assumptions that the model makes and we discuss methods for visualizing learning curves based on the model. We also report on an application of the model to data from a learning system for introductory programming; these experiments illustrate possibly misleading interpretation of model results due to differences in item difficulty. Overall, our results show that greater care has to be taken in the application of the model and in the interpretation of results obtained with the model.
ER -
EFFENBERGER, Tomáš, Radek PELÁNEK and Jaroslav ČECHÁK. Exploration of the Robustness and Generalizability of the Additive Factors Model. Online. In \textit{Proceedings of the 10th International Conference on Learning Analytics and Knowledge}. New York, NY, USA: Association for Computing Machinery, 2020, p.~472-479. ISBN~978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375491.
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