Adaptive Immediate Feedback for Block-Based Programming: Design and Evaluation

Theories on learning show that formative feedback that is immediate, specific, corrective, and positive is essential to improve novice students' motivation and learning. However, most prior work on programming feedback focuses on highlighting student's mistakes, or detecting failed test cases after they submit a solution. In this article, we present our adaptive immediate feedback (AIF) system, which uses a hybrid data-driven feedback generation algorithm to provide students with information on their progress, code correctness, and potential errors, as well as encouragement in the middle of programming. We also present an empirical controlled study using the AIF system across several programming tasks in a CS0 classroom. Our results show that the AIF system improved students' performance, and the proportion of students who fully completed the programming assignments, indicating increased persistence. Our results suggest that the AIF system has potential to scalably support students by giving them real-time formative feedback and the encouragement they need to complete assignments.