Impact of Methodological Choices on the Evaluation of Student Models

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Authors	EFFENBERGER Tomáš PELÁNEK Radek
Year of publication	2020
Type	Article in Proceedings
Conference	Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163.
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://doi.org/10.1007/978-3-030-52237-7_13
Doi	http://dx.doi.org/10.1007/978-3-030-52237-7_13
Keywords	adaptive learning; student modeling; intelligent tutoring systems; introductory programming
Description	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.
Related projects:	Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX