Mathematically high and low performances tell us different stories: Uncovering motivation-related factors via the ecological model

IF 3.8 1区 心理学 Q1 PSYCHOLOGY, EDUCATIONAL Learning and Individual Differences Pub Date : 2024-07-17 DOI:10.1016/j.lindif.2024.102513
Mehmet Hilmi Saglam , Talha Goktenturk
{"title":"Mathematically high and low performances tell us different stories: Uncovering motivation-related factors via the ecological model","authors":"Mehmet Hilmi Saglam ,&nbsp;Talha Goktenturk","doi":"10.1016/j.lindif.2024.102513","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigated how motivational factors contribute to math performance through the ecological model within exceptionally high and low achieving student populations. Using PISA 2018 data, a model including three layers of the ecological model were constructed to examine the ecological background of math performance for each group: exceptionally low &amp; high achievers. Employing structural equation modeling, the results revealed that high math performance was ecologically associated with factors: attitudes towards competition, growth mindset, motivation to master tasks, self-efficacy, teacher enthusiasm, teacher feedback, teacher support, value of school, and parents' emotional support. However, low math performance was related to a wider range of factors, including the aforementioned variables, as well as enjoyment of reading and learning goals. This research emphasizes a practical viewpoint that suggests using different interventions to maximize the potential of students in various positions on the math ability spectrum since the factors differ in explaining mathematically high and low performance.</p></div><div><h3>Educational relevance and implications statement</h3><p>In this study, we investigated motivation related factors that affect students with both high and low achievements in mathematics. Our results indicate that the factors associated with math performance differ between high and low achievers. This highlights the significance of need for differentiated educational strategies to maximize the potential of students across the math ability spectrum. This differentiation between the two groups may help in developing a tailored approach, enabling educators to promote a learning environment that is both inclusive and effective.</p></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"114 ","pages":"Article 102513"},"PeriodicalIF":3.8000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1041608024001067/pdfft?md5=cbc92cbf45de3a593eeff7f2c774e9c4&pid=1-s2.0-S1041608024001067-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning and Individual Differences","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1041608024001067","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EDUCATIONAL","Score":null,"Total":0}
引用次数: 0

Abstract

This study investigated how motivational factors contribute to math performance through the ecological model within exceptionally high and low achieving student populations. Using PISA 2018 data, a model including three layers of the ecological model were constructed to examine the ecological background of math performance for each group: exceptionally low & high achievers. Employing structural equation modeling, the results revealed that high math performance was ecologically associated with factors: attitudes towards competition, growth mindset, motivation to master tasks, self-efficacy, teacher enthusiasm, teacher feedback, teacher support, value of school, and parents' emotional support. However, low math performance was related to a wider range of factors, including the aforementioned variables, as well as enjoyment of reading and learning goals. This research emphasizes a practical viewpoint that suggests using different interventions to maximize the potential of students in various positions on the math ability spectrum since the factors differ in explaining mathematically high and low performance.

Educational relevance and implications statement

In this study, we investigated motivation related factors that affect students with both high and low achievements in mathematics. Our results indicate that the factors associated with math performance differ between high and low achievers. This highlights the significance of need for differentiated educational strategies to maximize the potential of students across the math ability spectrum. This differentiation between the two groups may help in developing a tailored approach, enabling educators to promote a learning environment that is both inclusive and effective.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
数学成绩高和成绩低告诉我们不同的故事:通过生态模型发现与学习动机有关的因素
本研究通过生态模型,调查了在成绩特别好和成绩特别差的学生群体中,动机因素是如何影响数学成绩的。利用 PISA 2018 数据,构建了一个包括三层生态模型的模型,以考察各群体数学成绩的生态背景:特别低&成绩优异者。通过结构方程模型,研究结果显示,数学成绩高与以下因素存在生态关联:竞争态度、成长心态、掌握任务的动机、自我效能感、教师热情、教师反馈、教师支持、学校价值以及父母的情感支持。然而,数学成绩低下与更广泛的因素有关,包括上述变量以及阅读乐趣和学习目标。这项研究强调了一种实用的观点,建议采用不同的干预措施,最大限度地发挥数学能力光谱上不同位置的学生的潜能,因为这些因素在解释数学成绩高低方面存在差异。我们的研究结果表明,与数学成绩相关的因素在成绩好的学生和成绩差的学生之间存在差异。这凸显了需要采取差异化教育策略的重要性,以最大限度地发挥不同数学能力学生的潜能。这两个群体之间的差异可能有助于制定有针对性的方法,使教育工作者能够促进既包容又有效的学习环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Learning and Individual Differences
Learning and Individual Differences PSYCHOLOGY, EDUCATIONAL-
CiteScore
6.60
自引率
2.80%
发文量
86
期刊介绍: Learning and Individual Differences is a research journal devoted to publishing articles of individual differences as they relate to learning within an educational context. The Journal focuses on original empirical studies of high theoretical and methodological rigor that that make a substantial scientific contribution. Learning and Individual Differences publishes original research. Manuscripts should be no longer than 7500 words of primary text (not including tables, figures, references).
期刊最新文献
The structure of adult thinking: A network approach to (meta)cognitive processing Ink and pixels: Impact of highlighting and reading self-efficacy on adolescents' cognitive load, epistemic emotions, and text comprehension Students' study activities before and after exam deadlines as predictors of performance in STEM courses: A multi-source data analysis The relationship between positive and painful emotions and cognitive load during an algebra learning task Idiographic learning analytics: Mapping of the ethical issues
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1