Extending the Assistance Model: Analyzing the Use of Assistance over Time

In the field of educational data mining, there are competing methods for predicting student performance. One involves building complex models, such as Bayesian networks with Knowledge Tracing (KT), or using logistic regression with Performance Factors Analysis (PFA). However, Wang and Heffernan showed that a raw data approach can be applied successfully to educational data mining with their results from what they called the Assistance Model (AM), which takes the number of attempts and hints required to answer the previous question correctly into account, which KT and PFA ignore. We extend their work by introducing a general framework for using raw data to predict student performance, and explore a new way of making predictions within this framework, called the Assistance Progress Model (APM). APM makes predictions based on the relationship between the assistance used on the two previous problems. KT, AM and APM are evaluated and compared to one another, as are multiple methods of ensembling them together. Finally, we discuss the importance of reporting multiple accuracy measures when evaluating student models