Behavioral Analytics for Distributed Practices in Programming Problem-Solving

This Research Full Paper aims to investigate the learning analytics of students' problem solving when working on distributed programming practices. Typical programming practice activities (i.e. assignments) in a lecture-dominant course may violate the principles of distributed retrieval practice. However, there are tradeoffs between managing depth and breadth of the content and classroom disruptions with the modern platforms and technologies. In this work, we investigate students' behavioral analytics in distributed programming practices. A classroom study was conducted in an introductory programming course and the learners' patterns were observed. Results showed that there were three distinct patterns found: affirmative, experimental, and surrendering. Better-performing students demonstrated more affirmative behaviors and fewer surrendering acts; Below-average students showed a lack of persistence in distributed practices. Additionally, the study reconfirmed the value of spacing effects on learning, which is the importance of spending time and to spreading the working sessions to solve diverse quizzes. Ineffective trial-and-error strategy and neglect the power of practices can be two alarming behaviors in distributed programming practices. Finally, predictive models of performance were presented based on the behavioral patterns.