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.

翻译：本研究应用Apriori算法分析数学辅导系统日志中与习得性无助（LH）相关的行为交互模式。从三个维度检验交互数据：LH水平（低 vs. 高）、系统干预（有 vs. 无）及问题解决结果（解决 vs. 未解决）。对完整数据集的分析表明，不使用提示跳过问题是与未解决结果关联最频繁的模式，而坚持行为（例如不跳过）整体上占比较小。按LH水平比较显示，低LH学生在问题解决与不跳过之间存在更强关联，且提示使用与已解决结果呈正相关。高LH学生表现出更明显的回避模式，跳过行为与未解决结果紧密关联。在系统干预条件比较中，无干预组学生在坚持-成功关联上具有最高提升度，而有干预组则表现出更强的跳过行为导致未解决结果的模式。针对特定结果的分析表明，所有组中不跳过行为始终与已解决问题相关，而无提示跳过则预测了未解决结果。本文讨论了实践意义及相关建议。