基于Apriori算法的数学辅导系统中习得性无助行为分析：按水平、干预和结果的行为模式
Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome
AI总结：
本文利用Apriori算法分析数学辅导系统中习得性无助行为模式，探讨不同水平、干预和结果下的行为特征，发现跳过问题而不使用提示与未解决问题相关，而坚持行为与解决问题更相关。
摘要：
本研究将Apriori算法应用于数学辅导系统日志中与习得性无助（LH）相关的交互模式分析。交互数据从三个维度进行考察：LH水平（低 vs 高）、基于系统的干预（有 vs 无）以及问题解决结果（解决 vs 未解决）。分析完整数据集显示，跳过问题而不使用提示是与未解决问题最频繁关联的模式，而坚持行为如不跳过则整体上不占主导地位。比较LH水平显示，低LH学生在问题解决与不跳过之间有更强的联系，且提示使用与解决结果有积极关联。高LH学生则表现出更多回避模式，跳过与未解决问题紧密相关。在基于系统干预条件的比较中，无干预学生在坚持-成功联系上具有最高提升值，而有干预组则表现出跳过行为与未解决问题更紧密的关联。结果特定分析显示，不跳过与解决所有问题一致相关，而跳过而不使用提示预测未解决问题。讨论了实际意义和建议。
英文摘要：
This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned helplessness (LH) in mathematics tutoring system logs. Interaction data were examined across three dimensions: LH level (low vs. high), system-based intervention (with vs. without), and problem-solving outcomes (solved vs. unsolved). The analysis of the complete dataset showed that skipping problems without using hints was the most frequent pattern linked to unsolved outcomes, while persistence behaviors such as not skipping were less dominant overall. Comparisons by LH level showed that low-LH students had stronger links between problem solving and not skipping, as well as positive associations between hint use and solved outcomes. High-LH students showed more avoidance patterns, with skipping strongly tied to unsolved outcomes. In the comparison of system-based intervention conditions, students without intervention had the highest lift for persistence-success links, while the with-intervention group had stronger patterns involving skipping behaviors leading to unsolved outcomes. Outcome-specific analysis showed that not skipping was consistently associated with solved problems across all groups, while skipping without hints predicted unsolved outcomes. Practical implications and recommendations are discussed.
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