Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems

Over the last decades, there have been a rich variety of approaches towards modeling student knowledge and skill within interactive learning environments. There have recently been several empirical comparisons as to which types of student models are better at predicting future performance, both within and outside of the interactive learning environment. However, these comparisons have produced contradictory results. Within this paper, we examine whether ensemble methods, which integrate multiple models, can produce prediction results comparable to or better than the best of nine student modeling frameworks, taken individually. We ensemble model predictions within a Cognitive Tutor for Genetics, at the level of predicting knowledge action-by-action within the tutor. We evaluate the predictions in terms of future performance within the tutor and on a paper post-test. Within this data set, we do not find evidence that ensembles of models are significantly better. Ensembles of models perform comparably to or slightly better than the best individual models, at predicting future performance within the tutor software. However, the ensembles of models perform marginally significantly worse than the best individual models, at predicting post-test performance.