Other formats:
BibTeX
LaTeX
RIS
@inproceedings{1780791,
author = {Čechák, Jaroslav and Pelánek, Radek},
address = {Cham},
booktitle = {International Conference on Artificial Intelligence in Education},
doi = {http://dx.doi.org/10.1007/978-3-030-78292-4_40},
editor = {Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania},
keywords = {Additive factor model; Student modeling; Simulation; Model comparison},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-030-78291-7},
pages = {500-511},
publisher = {Springer},
title = {Better Model, Worse Predictions: The Dangers in Student Model Comparisons},
year = {2021}
}
TY - JOUR
ID - 1780791
AU - Čechák, Jaroslav - Pelánek, Radek
PY - 2021
TI - Better Model, Worse Predictions: The Dangers in Student Model Comparisons
PB - Springer
CY - Cham
SN - 9783030782917
KW - Additive factor model
KW - Student modeling
KW - Simulation
KW - Model comparison
N2 - The additive factor model is a widely used tool for analyzing educational data, yet it is often used as an off-the-shelf solution without considering implementation details. A common practice is to compare multiple additive factor models, choose the one with the best predictive accuracy, and interpret the parameters of the model as evidence of student learning. In this work, we use simulated data to show that in certain situations, this approach can lead to misleading results. Specifically, we show how student skill distribution affects estimates of other model parameters.
ER -
ČECHÁK, Jaroslav and Radek PELÁNEK. Better Model, Worse Predictions: The Dangers in Student Model Comparisons. In Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania. \textit{International Conference on Artificial Intelligence in Education}. Cham: Springer, 2021, p.~500-511. ISBN~978-3-030-78291-7. Available from: https://dx.doi.org/10.1007/978-3-030-78292-4\_{}40.
|
