Looking Beyond Transfer Models: Finding Other Sources of Power for Student Models

Abstract

Student modeling plays an important role in educational research. Many techniques have been developed focusing on accurately estimating student performances. In this paper, using Performance Factors Analysis as our framework, we examine what components of the model enable us to better predict, and consequently better understand, student performance. Using transfer models to predict is very common across different student modeling techniques, as student proficiencies on those required skills are believed, to a large degree, to determine student performance. However, we found that problem difficulty is an even more important predictor than student knowledge of the required skills. In addition, we found that using student proficiencies across all skills works better than just using those skills thought relevant by the transfer model. We tested our proposed models with two transfer models of fine- and coarse-grain sizes; the results suggest that the improvement is not simply an illusion due to possible mistakes in associating skills with problems.