Building Models to Predict Hint-or-Attempt Actions of Students

Abstract

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to determine what a student’s first course of action will be when dealing with a problem, which may include attempting the problem or asking for help. Even though learner “course of actions” have been studied, it has mostly been used to predict correctness in succeeding problems. In this study, we present initial attempts at building models that utilize student action information: (a) the number of attempts taken and hints requested, and (b) history backtracks of hint request behavior, both of these are used to predict a student’s first course of action when working with problems in the ASSISTments tutoring system. Experimental results show that the models have reliable predictive accuracy when predicting students’ first course of action on the next problem.