Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome

基于 Apriori 算法的数学辅导中习得性无助分析：按水平、干预措施及结果划分的行为模式

Abstract: 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).

摘要： 本研究应用 Apriori 算法分析了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。研究从三个维度考察了交互数据：LH 水平（低 vs. 高）、基于系统的干预（有 vs. 无）以及问题解决结果（已解决 vs. 未解决）。

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.

对完整数据集的分析表明，在不使用提示的情况下跳过问题是导致未解决结果最频繁的模式，而诸如“不跳过”之类的坚持行为在总体上并不占主导地位。按 LH 水平进行的比较显示，低 LH 水平的学生在“解决问题”与“不跳过”之间有更强的关联，且“使用提示”与“解决结果”之间存在正相关。高 LH 水平的学生表现出更多的回避模式，其中跳过行为与未解决结果紧密相关。

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.

在基于系统的干预条件比较中，未接受干预的学生在“坚持-成功”关联上具有最高的提升度（lift），而接受干预的组别则表现出更强的“跳过行为导致未解决结果”的模式。针对结果的分析表明，在所有组别中，“不跳过”始终与“已解决问题”相关联，而“不使用提示即跳过”则预示着未解决的结果。文中还讨论了实际应用意义及相关建议。