Chenyu Hou , Gaoxia Zhu , Yuqin Yang , Seng-Chee Tan
{"title":"Exploring emotional dynamics between productive and improvable knowledge-building discourses","authors":"Chenyu Hou , Gaoxia Zhu , Yuqin Yang , Seng-Chee Tan","doi":"10.1016/j.compedu.2025.105395","DOIUrl":null,"url":null,"abstract":"<div><div>Knowledge Building (KB) is a pedagogical approach emphasizing students' collective responsibility to continuously improve their community knowledge. During KB activities, various emotions may arise due to students' diverse ideas, theory-building, as well as cognitive equilibrium and disequilibrium. Depending on the different development levels of the inquiry threads, the emotions may differ. An inquiry thread is a series of notes that address the same problem or topic. Despite increasing recognition of the importance of emotions in KB, or more generally, in computer-supported collaborative learning (CSCL), there is a lack of empirical studies systematically examining the dynamics of these emotions, particularly how they vary across different types of collaborative inquiry threads. Addressing this gap, this study analyzed 148 threads containing 6,240 notes from a learning science course at a public university over three years. Through clustering analysis, we identified productive and improvable KB inquiry threads recorded in the Knowledge Forum (KF) with productive threads, which are characterized by deeper cognitive efforts and more sustained discussion than improvable threads. Integrating three levels of analysis, namely frequency, lag-sequential analysis, and sequential pattern mining, we aim to comprehensively capture students’ emotional dynamics across different kinds of inquiry threads. Our study identified distinct emotional dynamics and constructed emotion evolution models for productive and improvable inquiry threads. Productive threads frequently exhibited transitions between negative emotions such as confusion, anxiety, and frustration, indicating their deeper cognitive engagement and suggesting that while students experienced challenges in reaching a consensus, they remained cognitively engaged in the inquiry. Conversely, improvable threads were characterized by sequences involving positive emotions like joy and curiosity, often appearing at the beginning of discussions or in threads that lacked depth, indicating that while initial interest was present, these discussions failed to evolve into meaningful inquiries. Emotional transitions from activating emotions (e.g., joy) to deactivating emotions (e.g., boredom) in improvable threads further suggest a disengagement from the discussion. These findings highlight the intricate emotional dynamics during learning activities and provide valuable insights for future research focused on enhancing productive discussions in CSCL environments through effective emotional regulation strategies.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"238 ","pages":"Article 105395"},"PeriodicalIF":8.9000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Education","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360131525001630","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Knowledge Building (KB) is a pedagogical approach emphasizing students' collective responsibility to continuously improve their community knowledge. During KB activities, various emotions may arise due to students' diverse ideas, theory-building, as well as cognitive equilibrium and disequilibrium. Depending on the different development levels of the inquiry threads, the emotions may differ. An inquiry thread is a series of notes that address the same problem or topic. Despite increasing recognition of the importance of emotions in KB, or more generally, in computer-supported collaborative learning (CSCL), there is a lack of empirical studies systematically examining the dynamics of these emotions, particularly how they vary across different types of collaborative inquiry threads. Addressing this gap, this study analyzed 148 threads containing 6,240 notes from a learning science course at a public university over three years. Through clustering analysis, we identified productive and improvable KB inquiry threads recorded in the Knowledge Forum (KF) with productive threads, which are characterized by deeper cognitive efforts and more sustained discussion than improvable threads. Integrating three levels of analysis, namely frequency, lag-sequential analysis, and sequential pattern mining, we aim to comprehensively capture students’ emotional dynamics across different kinds of inquiry threads. Our study identified distinct emotional dynamics and constructed emotion evolution models for productive and improvable inquiry threads. Productive threads frequently exhibited transitions between negative emotions such as confusion, anxiety, and frustration, indicating their deeper cognitive engagement and suggesting that while students experienced challenges in reaching a consensus, they remained cognitively engaged in the inquiry. Conversely, improvable threads were characterized by sequences involving positive emotions like joy and curiosity, often appearing at the beginning of discussions or in threads that lacked depth, indicating that while initial interest was present, these discussions failed to evolve into meaningful inquiries. Emotional transitions from activating emotions (e.g., joy) to deactivating emotions (e.g., boredom) in improvable threads further suggest a disengagement from the discussion. These findings highlight the intricate emotional dynamics during learning activities and provide valuable insights for future research focused on enhancing productive discussions in CSCL environments through effective emotional regulation strategies.
期刊介绍:
Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.