Adaptive Immediate Feedback Can Improve Novice Programming Engagement and Intention to Persist in Computer Science


Prior work suggests that novice programmers are greatly impacted by the feedback provided by their programming environments. While some research has examined the impact of feedback on student learning in programming, there is no work (to our knowledge) that examines the impact of adaptive immediate feedback within programming environments on students’ desire to persist in computer science (CS). In this paper, we integrate an adaptive immediate feedback (AIF) system into a block-based programming environment. Our AIF system is novel because it provides personalized positive and corrective feedback to students in real time as they work. In a controlled pilot study with novice high-school programmers, we show that our AIF system significantly increased students’ intentions to persist in CS, and that students using AIF had greater engagement (as measured by their lower idle time) compared to students in the control condition. Further, we found evidence that the AIF system may improve student learning, as measured by student performance in a subsequent task without AIF. In interviews, students found the system fun and helpful, and reported feeling more focused and engaged. We hope this paper spurs more research on adaptive immediate feedback and the impact of programming environments on students’ intentions to persist in CS.

Proceedings of the International Computing Education Research Conference (22.7% acceptance rate; 27/119 full papers)