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@inproceedings{1685465,
author = {Effenberger, Tomáš and Pelánek, Radek},
address = {Cham},
booktitle = {Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163.},
doi = {http://dx.doi.org/10.1007/978-3-030-52237-7_13},
editor = {Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E.},
keywords = {adaptive learning; student modeling; intelligent tutoring systems; introductory programming},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Cham},
isbn = {978-3-030-52236-0},
pages = {153-164},
publisher = {Springer},
title = {Impact of Methodological Choices on the Evaluation of Student Models},
url = {https://doi.org/10.1007/978-3-030-52237-7_13},
year = {2020}
}
TY - JOUR
ID - 1685465
AU - Effenberger, Tomáš - Pelánek, Radek
PY - 2020
TI - Impact of Methodological Choices on the Evaluation of Student Models
PB - Springer
CY - Cham
SN - 9783030522360
KW - adaptive learning
KW - student modeling
KW - intelligent tutoring systems
KW - introductory programming
UR - https://doi.org/10.1007/978-3-030-52237-7_13
L2 - https://doi.org/10.1007/978-3-030-52237-7_13
N2 - The evaluation of student models involves many methodological decisions, e.g., the choice of performance metric, data filtering, and cross-validation setting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our focus is not on these models per se but rather on the methodological choices necessary for performing these experiments. The results show, particularly, the importance of the choice of performance metric, including details of its computation and presentation.
ER -
EFFENBERGER, Tomáš a Radek PELÁNEK. Impact of Methodological Choices on the Evaluation of Student Models. Online. In Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. \textit{Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163.}. Cham: Springer, 2020, s.~153-164. ISBN~978-3-030-52236-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-52237-7\_{}13.
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