Modeling exploration strategies to predict student performance within a learning environment and beyond

Abstract

Modeling and predicting student learning is an important task in computer-based education. A large body of work has focused on representing and predicting student knowledge accurately. Existing techniques are mostly based on students' performance and on timing features. However, research in education, psychology and educational data mining has demonstrated that students' choices and strategies substantially influence learning. In this paper, we investigate the impact of students' exploration strategies on learning and propose the use of a probabilistic model jointly representing student knowledge and strategies. Our analyses are based on data collected from an interactive computer-based game. Our results show that exploration strategies are a significant predictor of the learning outcome. Furthermore, the joint models of performance and knowledge significantly improve the prediction accuracy within the game as well as on external post-test data, indicating that this combined representation provides a better proxy for learning.