Performance Factor Analysis

Performance Factor Analysis (PFA) is a predictive modeling framework that estimates student proficiency by tracking success and failure counts across specific skill sets.

PFA improves upon the standard Bayesian Knowledge Tracing (BKT) model by accounting for the specific difficulty of items and the cumulative impact of a student's prior attempts. Developed by researchers like Pavlik, Cen, and Koedinger (2009), the algorithm uses a logistic regression approach to weigh successful practice more heavily than failures. This sensitivity to individual learning curves makes it a staple in Intelligent Tutoring Systems (ITS) like Carnegie Learning's MATHia, where it predicts future performance with higher accuracy than models that ignore the frequency of past mistakes. It is the go-to choice for developers needing a robust, computationally efficient way to personalize educational content in real-time.

https://pactlab.org/pfa/