Publication details

Impact of Data Collection on Interpretation and Evaluation of Student Models

Authors

PELÁNEK Radek ŘIHÁK Jiří PAPOUŠEK Jan

Year of publication 2016
Type Article in Proceedings
Conference Proceedings of the Sixth International Conference on Learning Analytics & Knowledge
MU Faculty or unit

Faculty of Informatics

Citation
Web http://doi.acm.org/10.1145/2883851.2883868
Doi http://dx.doi.org/10.1145/2883851.2883868
Field Informatics
Keywords attition;bias;data sets;evaluation;parameter fitting;student modeling
Description 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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