From Gamification to Insights: Predicting Student Success in an Introductory Programming Course
Introductory programming courses remain challenging for many students, which motivates educators to adopt gamification to enhance engagement and learning. More recent work explores adaptive gamification, where game elements and task flow are tailored to individual learners. A key requirement for such adaptation is the ability to predict student success on upcoming tasks. Using a dataset of task attempts collected from a gamified introductory programming activity, we examine the predictive value of coarse-grained knowledge components, task difficulty, and dynamic student performance features. The results show that behavioral signals are substantially more informative than task properties: a student's prior success history and their position within a lesson sequence are the strongest predictors of future correctness. Although advanced topics such as file handling and structures are associated with increased failure rates, their impact is secondary to students' evolving engagement patterns. These findings highlight the role of momentum and practice effects in gamified programming environments and suggest that adaptive systems should prioritize real-time learner progression when providing instructional support. Dataset and the code for our experiments is available at https://osf.io/cajby.