Performance factors analysis - A new alternative to knowledge tracing
Abstract
Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used to compute latencies of actions. On the other hand, existing models used for educational data mining (e.g. Learning Factors Analysis (LFA)[2]) and model search do not tend to allow the creation of a "model overlay" that traces predictions for individual students with individual skills so as to allow the adaptive instruction to automatically remediate performance. Because these limitations make the transition from model search to model application in adaptive instruction more difficult, this paper describes our work to modify an existing data mining model so that it can also be used to select practice adaptively. We compare this new adaptive data mining model (PFA, Performance Factors Analysis) with two versions of LFA and then compare PFA with standard KT. © 2009 The authors and IOS Press. All rights reserved.
Publication Title
Frontiers in Artificial Intelligence and Applications
Recommended Citation
Pavlik, P., Cen, H., & Koedinger, K. (2009). Performance factors analysis - A new alternative to knowledge tracing. Frontiers in Artificial Intelligence and Applications, 200 (1), 531-538. https://doi.org/10.3233/978-1-60750-028-5-531
