Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures

Yue Gong, Joseph E. Beck, Neil T. Heffernan

Student modeling is very important for ITS due to its ability to make inferences about latent student attributes. Although knowledge tracing (KT) is a well-established technique, the approach used to fit the model is still a major issue as different model-fitting approaches lead to different parameter estimates. Performance Factor Analysis, a competing approach, predicts student performance based on the item difficulty and student historical performances. In this study, we compared these two models in terms of their predictive accuracy and parameter plausibility. For the knowledge tracing model, we also examined different model fitting algorithms: Expectation Maximization (EM) and Brute Force (BF). Our results showed that KT+EM is better than KT+BF and comparable with PFA in predictive accuracy. We also examined whether the models’ estimated parameter values were plausible. We found that by tweaking PFA, we were able to obtain more plausible parameters than with KT.

The final publication is available at Springer via