An Autonomous Analysis System of Learned Helplessness in Advanced Mathematics Learning Based on Multiple Linear Regression Algorithm

Yunjuan Li

GBP Proceedings Series, 2025, vol. 17, 160-167

Abstract: Advanced mathematics is a core course for science and engineering students at universities. The abstract and logical nature of the subject often presents significant challenges to students, leading to learning difficulties. As a result of prolonged frustration, some students may experience learned helplessness, which greatly diminishes their motivation and impedes academic progress. To address this issue, this paper proposes an autonomous analysis system designed to identify and mitigate learned helplessness in higher mathematics learning, utilizing a multiple linear regression algorithm. First, an index system was established, incorporating factors such as learning time, homework accuracy, test scores, classroom interaction frequency, and anxiety level. A quantitative model was then developed using multiple linear regression to analyze the relationship between learned helplessness and these various factors, enabling the assessment of students' levels of helplessness. Finally, an independent analysis system was designed to integrate data collection, model computation, result visualization, and the generation of intervention recommendations. Simulation results demonstrate that the system effectively identifies learned helplessness and provides targeted guidance tailored to students' varying levels of helplessness.

Keywords: multiple linear regression; higher mathematics; learned helplessness; autonomic analysis system; least squares method (search for similar items in EconPapers)
Date: 2025
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