Self-directed Extensive Reading Supported with GOAL System: Mining Sequential Patterns of Learning Behavior and Predicting Academic Performance

Self-directed learning (SDL) is an important skill in the 21st century, while the understanding of its process in behavior has not been well explored. Analysis of the sequential behavior patterns in SDL and the relations with students’ academic performance could help to advance our understanding of SDL in theory and practice. In this study, we mined the behavioral sequences of self-directed extensive reading from students’ learning and self-directed behavioral logs using differential pattern mining technique. Furthermore, we built models to predict students’ academic performance using the conventional behavior frequency features and the behavior sequence features. Experimental results identified 14 sequential patterns of SDL behaviors in the high-performance student group. The prediction model revealed the importance of sequential patterns in SDL behavior, which was built with an acceptable AUC. These findings suggested that several SDL strategies in behavior contribute to students’ academic performance, such as analysis learning status before planning, planning before learning, monitoring after learning.