Shuowen An, Si Zhang, Zhihui Cai, Wei Pan, Mingwei Li, Mingwen Tong
{"title":"揭示大学生合作解决问题过程中认知、元认知和社会过程的相互作用:三阶段分析框架","authors":"Shuowen An, Si Zhang, Zhihui Cai, Wei Pan, Mingwei Li, Mingwen Tong","doi":"10.1007/s11412-024-09429-0","DOIUrl":null,"url":null,"abstract":"<p>An in-depth analysis of collaborative problem solving (CPS) patterns contributes to understand team dynamics and effective paths to conflict resolution. However, there remains the lack of a perspective in the field of CPS research that organically combines the cognitive, meta-cognitive, and social-communicative dimensions. Moreover, the analysis of CPS sequences has primarily focused on the temporal dimension while overlooking the differences in spatial dimensions. To shed further light on the nature of CPS in computer-based environments, this study collected discourse data generated by 24 university students through an online synchronous chat tool. They were student teachers from a variety of disciplines (math, history, English, etc.) who were required to accomplish two tasks: instructional design and multimedia courseware development. Specifically, a three-stage analytical framework was proposed to code, cluster, and analyze these discourse data to further explore the differences in CPS patterns. We clustered time sequences by calculating the distance similarity metric via the dynamic time warping (DTW) method, which took into account both the spatial and temporal characteristics of the time sequences. Consequently, 16 time sequences of CPS processes were divided into 2 kinds of clusters (CPS subgroups), i.e., cluster 1 and cluster 2. From the statistical analysis, both clusters actively used the skills included in the meta-cognitive dimensions. Cluster 1 was oriented toward the solution of the problem whereas cluster 2 focused primarily on the requirements of the collaborative problem itself. From the process mining analysis, solution-driven cluster 1 tended to focus on expressing specific ideas and evaluating and summarizing them, intermittently monitoring and regulating task progress. Problem-driven cluster 2 tended to express specific ideas intermittently, and lacked the process of summarizing and evaluating different ideas to further filter out the best solutions. Finally, we summarized the implications of this study from theoretical and practical perspectives and discussed future research directions with regard to the limitations of this study.</p>","PeriodicalId":47189,"journal":{"name":"International Journal of Computer-Supported Collaborative Learning","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Revealing the interplay of cognitive, meta-cognitive, and social processes in university students’ collaborative problem solving: a three-stage analytical framework\",\"authors\":\"Shuowen An, Si Zhang, Zhihui Cai, Wei Pan, Mingwei Li, Mingwen Tong\",\"doi\":\"10.1007/s11412-024-09429-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>An in-depth analysis of collaborative problem solving (CPS) patterns contributes to understand team dynamics and effective paths to conflict resolution. However, there remains the lack of a perspective in the field of CPS research that organically combines the cognitive, meta-cognitive, and social-communicative dimensions. Moreover, the analysis of CPS sequences has primarily focused on the temporal dimension while overlooking the differences in spatial dimensions. To shed further light on the nature of CPS in computer-based environments, this study collected discourse data generated by 24 university students through an online synchronous chat tool. They were student teachers from a variety of disciplines (math, history, English, etc.) who were required to accomplish two tasks: instructional design and multimedia courseware development. Specifically, a three-stage analytical framework was proposed to code, cluster, and analyze these discourse data to further explore the differences in CPS patterns. We clustered time sequences by calculating the distance similarity metric via the dynamic time warping (DTW) method, which took into account both the spatial and temporal characteristics of the time sequences. Consequently, 16 time sequences of CPS processes were divided into 2 kinds of clusters (CPS subgroups), i.e., cluster 1 and cluster 2. From the statistical analysis, both clusters actively used the skills included in the meta-cognitive dimensions. Cluster 1 was oriented toward the solution of the problem whereas cluster 2 focused primarily on the requirements of the collaborative problem itself. From the process mining analysis, solution-driven cluster 1 tended to focus on expressing specific ideas and evaluating and summarizing them, intermittently monitoring and regulating task progress. Problem-driven cluster 2 tended to express specific ideas intermittently, and lacked the process of summarizing and evaluating different ideas to further filter out the best solutions. Finally, we summarized the implications of this study from theoretical and practical perspectives and discussed future research directions with regard to the limitations of this study.</p>\",\"PeriodicalId\":47189,\"journal\":{\"name\":\"International Journal of Computer-Supported Collaborative Learning\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computer-Supported Collaborative Learning\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1007/s11412-024-09429-0\",\"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":"International Journal of Computer-Supported Collaborative Learning","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s11412-024-09429-0","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}
Revealing the interplay of cognitive, meta-cognitive, and social processes in university students’ collaborative problem solving: a three-stage analytical framework
An in-depth analysis of collaborative problem solving (CPS) patterns contributes to understand team dynamics and effective paths to conflict resolution. However, there remains the lack of a perspective in the field of CPS research that organically combines the cognitive, meta-cognitive, and social-communicative dimensions. Moreover, the analysis of CPS sequences has primarily focused on the temporal dimension while overlooking the differences in spatial dimensions. To shed further light on the nature of CPS in computer-based environments, this study collected discourse data generated by 24 university students through an online synchronous chat tool. They were student teachers from a variety of disciplines (math, history, English, etc.) who were required to accomplish two tasks: instructional design and multimedia courseware development. Specifically, a three-stage analytical framework was proposed to code, cluster, and analyze these discourse data to further explore the differences in CPS patterns. We clustered time sequences by calculating the distance similarity metric via the dynamic time warping (DTW) method, which took into account both the spatial and temporal characteristics of the time sequences. Consequently, 16 time sequences of CPS processes were divided into 2 kinds of clusters (CPS subgroups), i.e., cluster 1 and cluster 2. From the statistical analysis, both clusters actively used the skills included in the meta-cognitive dimensions. Cluster 1 was oriented toward the solution of the problem whereas cluster 2 focused primarily on the requirements of the collaborative problem itself. From the process mining analysis, solution-driven cluster 1 tended to focus on expressing specific ideas and evaluating and summarizing them, intermittently monitoring and regulating task progress. Problem-driven cluster 2 tended to express specific ideas intermittently, and lacked the process of summarizing and evaluating different ideas to further filter out the best solutions. Finally, we summarized the implications of this study from theoretical and practical perspectives and discussed future research directions with regard to the limitations of this study.
期刊介绍:
An official publication of the International Society of the Learning Sciences, the International Journal of Computer-Supported Collaborative Learning (IJCSCL) fosters a deep understanding of the nature, theory, and practice of computer-supported collaborative learning (CSCL). The journal serves as a forum for experts from such disciplines as education, computer science, information technology, psychology, communications, linguistics, anthropology, sociology, and business. Articles investigate how to design the technological settings for collaboration and how people learn in the context of collaborative activity.