计算机支持的协作学习研究综述:问题与可能性

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Rogers Kaliisa, Sonsoles López-Pernas, Kamila Misiejuk, Crina Damşa, Márta Sobocinski, Sanna Järvelä, Mohammed Saqr
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引用次数: 0

摘要

本研究通过分析1990年至2022年间发表的6388份文献,描绘了计算机支持的协作学习(CSCL)研究的演变和状态。研究结果强调了核心学者群体的持续参与,以及该领域在地理上集中在西方国家,特别是美国和欧洲。虽然该领域仍然富有成效和多样化,但最近的趋势反映出越来越多的人强调将学习分析、增强和虚拟现实以及人工智能(AI)等新兴技术整合到协作学习环境中。该研究揭示了CSCL内部认识论和方法多样性与理论一致性需求之间的紧张关系。这种多样性使该领域能够适应和创新,但也引起了人们对碎片化和失去统一身份的风险的担忧。例如,虽然脚本仍然是一个基础主题,但关于平衡教学指导和学习者代理以促进富有成效的协作的争论仍然存在。展望未来,该研究强调了CSCL需要发展综合理论框架,将其丰富的历史基础与大规模、分布式和技术介导的协作所带来的挑战联系起来。解决这些挑战,例如将人工智能创新与现有理论结合起来,并确保积累知识,对于该领域在理解复杂教育环境中的协作学习方面保持其相关性和影响力的能力至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Topical Review of Research in Computer-Supported Collaborative Learning: Questions and Possibilities
This study maps the evolution and state of Computer-Supported Collaborative Learning (CSCL) research, analyzing 6388 documents published between 1990 and 2022. The findings highlight the sustained engagement of a core group of scholars and the field's geographic concentration in Western countries, particularly the USA and Europe. While the field remains productive and diverse, recent trends reflect a growing emphasis on integrating emerging technologies such as learning analytics, augmented and virtual reality, and artificial intelligence (AI) into collaborative learning contexts. The study uncovers a tension within CSCL between its epistemological and methodological diversity and the need for theoretical coherence. This diversity has allowed the field to adapt and innovate but raises concerns about fragmentation and the risk of losing a unified identity. For example, while scripting remains a foundational topic, debates persist on balancing instructional guidance with learner agency to foster productive collaboration. Looking ahead, the study underscores the need for CSCL to develop integrative theoretical frameworks that bridge its rich historical foundations with the challenges posed by large-scale, distributed, and technology-mediated collaboration. Addressing these challenges, such as aligning AI innovations with existing theories and ensuring cumulative knowledge-building, will be critical for the field's ability to sustain its relevance and influence in understanding collaborative learning in complex educational environments.
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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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