Beyond personality traits: a motivation-self-regulation model of mathematical problem-solving through self-efficacy and mathematical thinking

超越人格特质:基于自我效能和数学思维的数学问题解决动机-自我调节模型

阅读:1

Abstract

PURPOSE: This study aims to understand how learning-related traits and process variables collectively influence high school students' mathematical problem-solving ability; and to verify the mediating roles of mathematical self-efficacy and mathematical thinking. METHODS: Based on questionnaire and mathematical performance test data from 1,183 high school students in Shanghai, the study employs partial least squares structural equation modeling (PLS-SEM) for analysis. RESULTS: The results show that the model has high explanatory power for mathematical self-efficacy, mathematical thinking, and mathematical problem-solving ability; motivation to math and self-regulated learning are core upstream predictors of problem-solving performance, while mathematical self-efficacy and mathematical thinking serve as the closest direct predictors of problem-solving and significantly mediate the effects of motivation and self-regulated learning on problem-solving ability. In contrast, personality traits exhibit relatively weak direct and mediating effects after controlling for prior academic performance and the aforementioned learning variables. IMPLICATIONS: The contribution of this study lies not only in integrating motivation, self-regulation, affective beliefs, and higher-order mathematical thinking into a unified structural framework, but also in clarifying the relative explanatory roles of distal personality traits and proximal learning processes, showing that mathematical problem-solving is shaped through both affective-belief and cognitive pathways, and demonstrating that the explanatory advantage within the present model lies primarily in domain-specific, modifiable learning processes rather than broad personality dispositions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。