通过追踪解题路径，发现即时重试更有利于学习效果。
Let Me Try Again: Examining Replay Behavior by Tracing Students' Latent Problem-Solving Pathways
- 用马尔可夫模型分析777名初中生的游戏学习日志，识别出四种隐状态。
- 即时重试与更高概念理解、灵活思维和成绩相关，延迟重试则效果差。
- 研究揭示了重试时机的重要性，适合教育技术与学习分析领域者参考。
先前研究显示，游戏化学习环境中学生的解题路径反映了其概念理解、程序性知识和灵活性。重试行为可能表明积极的挣扎或更广泛的探索，从而促进深度学习。然而，对这些路径在问题序列中的演变过程，以及重试时机与其他解题策略如何影响近期和远期学习成果，仍知之甚少。本研究基于777名七年级学生在《From Here to There!》游戏化学习平台上的日志数据，运用马尔可夫链与隐马尔可夫模型（HMM）进行分析。结果显示，在问题序列内部，学生常停留在某状态或在成功后立即重试；跨问题间，强自转移表明稳定的战略路径。HMM识别出四种隐状态：不完整主导型、最优结束型、重试型与混合型。回归分析表明，重试主导型与最优结束型状态的参与度显著预测更高的概念知识、灵活性与表现，优于不完整主导型。即时重试始终与良好学习成果相关，而延迟重试则与非重试组相比弱相关甚至负相关。结果表明，数字学习中的重试并非普遍有益，其效果取决于时机，即时重试有助于提升灵活性与更具生产性的探索。
原文摘要 · Abstract (English)
Prior research has shown that students' problem-solving pathways in game-based learning environments reflect their conceptual understanding, procedural knowledge, and flexibility. Replay behaviors, in particular, may indicate productive struggle or broader exploration, which in turn foster deeper learning. However, little is known about how these pathways unfold sequentially across problems or how the timing of replays and other problem-solving strategies relates to proximal and distal learning outcomes. This study addresses these gaps using Markov Chains and Hidden Markov Models (HMMs) on log data from 777 seventh graders playing the game-based learning platform of From Here to There!. Results show that within problem sequences, students often persisted in states or engaged in immediate replay after successful completions, while across problems, strong self-transitions indicated stable strategic pathways. Four latent states emerged from HMMs: Incomplete-dominant, Optimal-ending, Replay, and Mixed. Regression analyses revealed that engagement in replay-dominant and optimal-ending states predicted higher conceptual knowledge, flexibility, and performance compared with the Incomplete-dominant state. Immediate replay consistently supported learning outcomes, whereas delayed replay was weakly or negatively associated in relation to Non-Replay. These findings suggest that replay in digital learning is not uniformly beneficial but depends on timing, with immediate replay supporting flexibility and more productive exploration.
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