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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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