A Differential Approach for Identifying Important Student Learning Behavior Patterns with Evolving Usage over Time.

Effective design and improvement of dynamic feedback in computer-based learning environments requires the ability to assess the effectiveness of a variety of feedback options, not only in terms of overall performance and learning, but also in terms of more subtle effects on students' learning behavior and understanding. In this paper, we present a novel interestingness measure, and corresponding data mining and visualization approach, which aids the investigation and understanding of students' learning behaviors. The presented approach identifies sequential patterns of activity that distinguish groups of students (e.g., groups that received different feedback during extended, complex learning activities) by differences in both total behavior pattern usage and evolution of pattern usage over time. We demonstrate the utility of this technique through application to student learning activity data from a recent experiment with the Betty's Brain learning environment and four different feedback and learning scaffolding conditions.