AI 中文总结
本研究在数字谜题式决策树构建学习游戏中加入面向策略的自适应反馈模块，通过分析205名学习者的游戏数据，证实该反馈可培养学生的结构化问题解决技能，为中学ML学习环境设计提供参考。
AI 中文摘要
培养学生形成系统性问题解决策略是计算教育的核心目标，在新兴的机器学习（ML）教育领域尤为重要。尽管探索性方法在ML学习任务中很常见，但培养结构化问题解决策略的发展与坚持仍具挑战性，因为这需要大量元认知调节和坚持性，导致学习者常常退回到探索性试错行为。为应对这一挑战，我们为一款基于数字谜题的决策树构建学习游戏添加了自适应反馈模块，该模块基于对学习者问题解决策略的持续评估生成个性化消息。本研究在早期基线研究的基础上，探究这种面向策略的反馈如何塑造学生的问题解决过程。为此，我们使用了录屏视频数据和游戏玩法日志（N=205，约55小时的游戏素材），以实现对学习者策略行为、其坚持性及转变的细粒度洞察。研究结果表明，面向策略的反馈可支持决策树构建中结构化问题解决技能的发展，并为中学计算教育中培养可迁移能力的ML学习环境设计提供参考。
英文摘要
Enabling students to develop systematic problem-solving strategies is a central goal in computing education and of particular relevance in the emerging field of machine learning (ML) education. While exploratory approaches are common in ML learning tasks, fostering the development and persistence of structured problem-solving strategies remains challenging, as these demand considerable metacognitive regulation and persistence, causing learners to often revert to exploratory trial-and-error behavior. To address this challenge, we augmented a digital puzzle-based learning game for decision tree construction with an adaptive feedback module generating individualized messages based on the continuous evaluation of learners' problem-solving strategies. Building on an earlier baseline study, the present work investigates how this strategy-oriented feedback shapes students' problem-solving processes. For this purpose, screencast video data and gameplay logs (N=205, approx. 55 hours of gameplay footage) are used to enable fine-grained insights into learners' strategic behavior, its persistence, and transitions. The findings demonstrate how strategy-oriented feedback can support the development of structured problem-solving skills in decision tree construction and inform the design of ML learning environments that foster transferable competencies in secondary computing education.
CommentsThis is the author's version of a paper accepted for publication at the 2026 Workshop in Primary and Secondary Computing Education Research (WiPSCE 2026). The final authenticated version will be published in the ACM International Conference Proceedings Series and will be available via the ACM Digital Library
