Impact of Data Collection on Interpretation and Evaluation of Student Models

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Publikace nespadá pod Filozofickou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.

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PELÁNEK Radek ŘIHÁK Jiří PAPOUŠEK Jan

Rok publikování 2016
Druh Článek ve sborníku
Konference Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://doi.acm.org/10.1145/2883851.2883868
Doi http://dx.doi.org/10.1145/2883851.2883868
Obor Informatika
Klíčová slova attition;bias;data sets;evaluation;parameter fitting;student modeling
Popis Student modeling techniques are evaluated mostly using historical data. Researchers typically do not pay attention to details of the origin of the used data sets. However, the way data are collected can have important impact on evaluation and interpretation of student models. We discuss in detail two ways how data collection in educational systems can influence results: mastery attrition bias and adaptive choice of items. We systematically discuss previous work related to these biases and illustrate the main points using both simulated and real data. We summarize specific consequences for practice -- not just for doing evaluation of student models, but also for data collection and publication of data sets.
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