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

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Title: Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome.
Authors: Miranda, John Paul P.1 jppmiranda@pampangastateu.edu.ph
Source: Electronic Journal of e-Learning. 2026, Vol. 24 Issue 2, p149-156. 9p.
Subject Terms: *Helplessness (Psychology), *Human behavior, *Problem solving, *Tutors & tutoring, Apriori algorithm, Operant behavior
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). 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. [ABSTRACT FROM AUTHOR]
Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome.
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  Data: <searchLink fieldCode="AR" term="%22Miranda%2C+John+Paul+P%2E%22">Miranda, John Paul P.</searchLink><relatesTo>1</relatesTo><i> jppmiranda@pampangastateu.edu.ph</i>
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  Data: <searchLink fieldCode="JN" term="%22Electronic+Journal+of+e-Learning%22">Electronic Journal of e-Learning</searchLink>. 2026, Vol. 24 Issue 2, p149-156. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Helplessness+%28Psychology%29%22">Helplessness (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Human+behavior%22">Human behavior</searchLink><br />*<searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br />*<searchLink fieldCode="DE" term="%22Tutors+%26+tutoring%22">Tutors & tutoring</searchLink><br /><searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Operant+behavior%22">Operant behavior</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Electronic Journal of e-Learning is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.34190/ejel.24.2.4602
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        Text: English
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      – SubjectFull: Human behavior
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      – SubjectFull: Problem solving
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      – SubjectFull: Tutors & tutoring
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      – SubjectFull: Apriori algorithm
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              Text: 2026
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