{"title":"人工智能支持的思考-配对-分享教学对学生二次方程学习动机和问题解决能力的影响：社会建构主义视角","authors":"Belaynesh Kassanew, Cathy Weng, Melese Astatke, Yamral Kassanew","doi":"10.1002/jcal.70271","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Quadratic equations are a cornerstone of algebra and serve as a gateway to advanced mathematical concepts. Despite their importance, students frequently struggle with conceptual understanding, procedural accuracy, and sustained motivation when learning mathematics, particularly quadratic equations. Traditional instruction often fails to address these difficulties, leading to persistent misconceptions and disengagement. Artificial intelligence (AI) shows promise in offering adaptive feedback and customised support, but research mainly focuses on its role in helping individual learners. On the other hand, the Think-Pair-Share (TPS) model, grounded in Vygotsky's social constructivist theory, has been proven effective in fostering collaboration, critical thinking, and communication. However, limited studies have explored how AI can be systematically integrated into structured collaborative learning approaches such as TPS to enhance both problem-solving and motivation.</p>\n </section>\n \n <section>\n \n <h3> Rationale and Objectives</h3>\n \n <p>This study introduces a novel instructional model that embeds AI scaffolding into each phase of the TPS process. Rather than replacing teacher facilitation, AI functions as a supportive scaffold that delivers individualised prompts during the <i>Think</i> stage, facilitates peer negotiation in the <i>Pair</i> stage, and provides clarification or reinforcement in the <i>Share</i> stage. This integration ensures consistent, contextualised guidance while maintaining the collaborative essence of TPS. The study therefore, aims to examine the effectiveness of AI-supported TPS instruction in enhancing junior high school students' problem-solving skills and motivation in learning quadratic equations.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This study employed a quasi-experimental design with a non-equivalent control group. A total of 43 junior high school students participated, with the experimental group receiving AI-enhanced TPS instruction and the control group following traditional TPS instruction with online Google searches. Data were collected using two instruments: a problem-solving test to measure students' learning outcomes and a mathematics motivation questionnaire to assess intrinsic motivation, interest, and self-efficacy. The data were analysed using analysis of covariance (ANCOVA) to control for pre-test differences and determine the impact of the intervention.</p>\n </section>\n \n <section>\n \n <h3> Results and Conclusions</h3>\n \n <p>The findings indicate significant improvements in both problem-solving skills and motivation for the experimental group compared to the control group. Integrating AI tools within the TPS framework not only enhances cognitive outcomes but also fosters greater student engagement, highlighting the potential of technology to transform mathematics education.</p>\n </section>\n </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"42 4","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Impact of AI-Supported Think-Pair-Share Instruction on Students' Problem-Solving Skills and Motivation in Learning Quadratic Equations: A Social Constructivist Perspective\",\"authors\":\"Belaynesh Kassanew, Cathy Weng, Melese Astatke, Yamral Kassanew\",\"doi\":\"10.1002/jcal.70271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Quadratic equations are a cornerstone of algebra and serve as a gateway to advanced mathematical concepts. Despite their importance, students frequently struggle with conceptual understanding, procedural accuracy, and sustained motivation when learning mathematics, particularly quadratic equations. Traditional instruction often fails to address these difficulties, leading to persistent misconceptions and disengagement. Artificial intelligence (AI) shows promise in offering adaptive feedback and customised support, but research mainly focuses on its role in helping individual learners. On the other hand, the Think-Pair-Share (TPS) model, grounded in Vygotsky's social constructivist theory, has been proven effective in fostering collaboration, critical thinking, and communication. However, limited studies have explored how AI can be systematically integrated into structured collaborative learning approaches such as TPS to enhance both problem-solving and motivation.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Rationale and Objectives</h3>\\n \\n <p>This study introduces a novel instructional model that embeds AI scaffolding into each phase of the TPS process. Rather than replacing teacher facilitation, AI functions as a supportive scaffold that delivers individualised prompts during the <i>Think</i> stage, facilitates peer negotiation in the <i>Pair</i> stage, and provides clarification or reinforcement in the <i>Share</i> stage. This integration ensures consistent, contextualised guidance while maintaining the collaborative essence of TPS. The study therefore, aims to examine the effectiveness of AI-supported TPS instruction in enhancing junior high school students' problem-solving skills and motivation in learning quadratic equations.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>This study employed a quasi-experimental design with a non-equivalent control group. A total of 43 junior high school students participated, with the experimental group receiving AI-enhanced TPS instruction and the control group following traditional TPS instruction with online Google searches. Data were collected using two instruments: a problem-solving test to measure students' learning outcomes and a mathematics motivation questionnaire to assess intrinsic motivation, interest, and self-efficacy. The data were analysed using analysis of covariance (ANCOVA) to control for pre-test differences and determine the impact of the intervention.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results and Conclusions</h3>\\n \\n <p>The findings indicate significant improvements in both problem-solving skills and motivation for the experimental group compared to the control group. Integrating AI tools within the TPS framework not only enhances cognitive outcomes but also fosters greater student engagement, highlighting the potential of technology to transform mathematics education.</p>\\n </section>\\n </div>\",\"PeriodicalId\":48071,\"journal\":{\"name\":\"Journal of Computer Assisted Learning\",\"volume\":\"42 4\",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2026-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computer Assisted Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jcal.70271\",\"RegionNum\":2,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Assisted Learning","FirstCategoryId":"95","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcal.70271","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
The Impact of AI-Supported Think-Pair-Share Instruction on Students' Problem-Solving Skills and Motivation in Learning Quadratic Equations: A Social Constructivist Perspective
Background
Quadratic equations are a cornerstone of algebra and serve as a gateway to advanced mathematical concepts. Despite their importance, students frequently struggle with conceptual understanding, procedural accuracy, and sustained motivation when learning mathematics, particularly quadratic equations. Traditional instruction often fails to address these difficulties, leading to persistent misconceptions and disengagement. Artificial intelligence (AI) shows promise in offering adaptive feedback and customised support, but research mainly focuses on its role in helping individual learners. On the other hand, the Think-Pair-Share (TPS) model, grounded in Vygotsky's social constructivist theory, has been proven effective in fostering collaboration, critical thinking, and communication. However, limited studies have explored how AI can be systematically integrated into structured collaborative learning approaches such as TPS to enhance both problem-solving and motivation.
Rationale and Objectives
This study introduces a novel instructional model that embeds AI scaffolding into each phase of the TPS process. Rather than replacing teacher facilitation, AI functions as a supportive scaffold that delivers individualised prompts during the Think stage, facilitates peer negotiation in the Pair stage, and provides clarification or reinforcement in the Share stage. This integration ensures consistent, contextualised guidance while maintaining the collaborative essence of TPS. The study therefore, aims to examine the effectiveness of AI-supported TPS instruction in enhancing junior high school students' problem-solving skills and motivation in learning quadratic equations.
Methods
This study employed a quasi-experimental design with a non-equivalent control group. A total of 43 junior high school students participated, with the experimental group receiving AI-enhanced TPS instruction and the control group following traditional TPS instruction with online Google searches. Data were collected using two instruments: a problem-solving test to measure students' learning outcomes and a mathematics motivation questionnaire to assess intrinsic motivation, interest, and self-efficacy. The data were analysed using analysis of covariance (ANCOVA) to control for pre-test differences and determine the impact of the intervention.
Results and Conclusions
The findings indicate significant improvements in both problem-solving skills and motivation for the experimental group compared to the control group. Integrating AI tools within the TPS framework not only enhances cognitive outcomes but also fosters greater student engagement, highlighting the potential of technology to transform mathematics education.
期刊介绍：
The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope
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