A Variant of Performance Factors Analysis Model for Categorization
Cao, Meng, Philip I. Pavlik · Zenodo (CERN European Organization for Nuclear Research) · 2022
Many models of categorization focus on how people form and use knowledge of categories and make predictions about hu-man 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.