Exploring emotional dynamics between productive and improvable knowledge-building discourses

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chenyu Hou , Gaoxia Zhu , Yuqin Yang , Seng-Chee Tan
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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.
探索富有成效和可改进的知识建设话语之间的情感动态
知识建构(Knowledge Building, KB)是一种教学方法,强调学生的集体责任,以不断提高他们的社区知识。在KB活动中,由于学生思想观念的多样化、理论建构的多样化以及认知的均衡与不均衡,会产生各种各样的情绪。根据查询线程的不同开发水平,情绪可能会有所不同。查询线程是针对相同问题或主题的一系列注释。尽管越来越多的人认识到情绪在知识库中的重要性,或者更普遍地说,在计算机支持的协作学习(CSCL)中,缺乏系统地检查这些情绪动态的实证研究,特别是它们在不同类型的协作探究线程中如何变化。为了解决这一问题,本研究分析了一所公立大学三年来学习科学课程的148个帖子,其中包含6240个笔记。通过聚类分析,我们确定了知识论坛(Knowledge Forum, KF)中记录的生产性和可改进的知识库查询线程,这些线程具有比可改进线程更深入的认知努力和更持久的讨论的特征。整合三个层次的分析,即频率、滞后序列分析和顺序模式挖掘,我们的目标是全面捕捉学生在不同类型的探究线索中的情感动态。我们的研究确定了不同的情感动态,并为富有成效和可改进的探究线索构建了情感进化模型。富有成效的线索经常表现出在困惑、焦虑和沮丧等负面情绪之间的过渡,表明他们更深层次的认知参与,并表明尽管学生在达成共识方面遇到了挑战,但他们仍然在认知上参与了探究。相反,可改进的线程的特点是包含积极情绪(如快乐和好奇)的序列,通常出现在讨论的开始或缺乏深度的线程中,这表明尽管存在最初的兴趣,但这些讨论未能演变成有意义的询问。在可改善的线索中,从活跃情绪(如快乐)到不活跃情绪(如无聊)的情绪转变进一步表明从讨论中脱离出来。这些发现突出了学习活动中复杂的情绪动态,并为未来通过有效的情绪调节策略加强CSCL环境中富有成效的讨论的研究提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
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.
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