Learning from Teachers: AI-Driven Feedback for a High School Python Serious Game
Résumé
Research has shown that serious games can effectively support programming learning in K-12 education. However, some students may have difficulties learning with such games and, in those cases, can benefit from dedicated support. Unfortunately, K-12 teachers cannot always provide this support in a timely manner, especially due to a lack of time in crowded high school classes. Thus, to facilitate the large-scale use of programming serious games in high school courses, we explore the feasibility of augmenting such games with AI-driven adaptive feedback designed to address some of the learners' difficulties while reducing teachers' burden. Specifically, we investigate the design of an AI-driven feedback policy that can predict, during interaction with a serious game, what type of formative feedback is suitable to address students' difficulties. To build this policy, we conducted a user study in regular 10th grade Python classes to collect labels on how experienced teachers (N = 7) support their students (N = 215). We trained machine learning classifiers to predict these labels using students' interaction behaviours and code. Our results show that teachers extensively use several feedback types and that Random Forest classifiers can significantly outperform relevant baselines in predicting them, achieving a F1 score, precision, and recall of up to 0.70. We also shed light on the behaviours that best explain these predictions, and found that features related to both the students' code and their interaction with the game elements relevant to the current game level matter. We then discuss the implications of our work for designing personalized feedback at scale in programming serious games.
• Applied computing → Interactive learning environments; • Computing methodologies → Supervised learning by classification; • Human-centered computing → Field studies.
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