A Variant of Performance Factors Analysis Model for Categorization
Abstract
Many models of categorization focus on how people form and use knowledge of categories and make predictions about human categorization behaviors [19]. However, few (if any) of them implement these theories into item selection algorithms for category training. The performance Factors Analysis (PFA) model is an alternative to the Bayesian Knowledge Tracing model that tracks students’ learning of knowledge components and can be implemented into adaptive practice algorithms [17]. PFA-Difficulty model has been built to select items based on their difficulty level adaptively [4]. This paper describes how we are working to incorporate categorization theories into the PFA model so that it can be used for item selection. We used experiment data of Mandarin tone categorization training to test the model and suggest the implications of the results for item selection.
Publication Title
Proceedings of the International Conference on Educational Data Mining
Recommended Citation
Cao, M., & Pavlik, P. (2022). A Variant of Performance Factors Analysis Model for Categorization. Proceedings of the International Conference on Educational Data Mining https://doi.org/10.5281/zenodo.6852974
