Paper applies Apriori association rule mining to analyze learned helplessness behavioral patterns in math tutoring system logs across three dimensions: LH level, intervention status, and problem-solving outcomes. Finds that problem avoidance (skipping without hints) correlates with unsolved problems, while persistence correlates with success—with distinct patterns between low-LH and high-LH students.
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Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome
Apriori mining of tutoring logs reveals learned helplessness behavioral fingerprints—students exhibiting problem avoidance fail more frequently, while persistent students succeed, with distinct patterns between high-LH and low-LH cohorts.
Thursday, April 30, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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