AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review

IF 9.6 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Fan Ouyang, Liyin Zhang
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引用次数: 0

Abstract

Artificial intelligence (AI) has brought new ways for implementing learning analytics in computer-supported collaborative learning (CSCL). However, there is a lack of literature reviews that focus on AI-driven learning analytics applications and tools in CSCL contexts. To fill the gap, this systematic review provides an overview of the goals, characteristics, and effects of existing AI-driven learning analytics applications and tools in CSCL. According to the screening criteria, out of the 2607 initially identified articles between 2004 and 2023, 26 articles are included for final synthesis. Our results show that existing tools primarily focus on students’ cognitive engagement. Existing tools primarily utilize communicative discourse, behavioral, and evaluation data to present results and visualizations. Despite various formats of feedback are provided in existing tools, there is a lack of design principles to guide the tool design and development process. Moreover, although AI techniques have been applied for presenting statistical information, there is a lack of providing alert or suggestive information in existing tools or applications. Compared with the positive impacts on collaborative learning, our results indicate a lack of support for instructional interventions in existing tools. This systematic review proposes the following theoretical, technological, and practical implications: (1) the integration of educational and learning theories into AI-driven learning analytics applications and tools; (2) the adoption of advanced AI technologies to collect, analyze, and interpret multi-source and multimodal data; and (3) the support for instructors with actionable suggestions and instructional interventions. Based on our findings, we provide further directions on how to design, analyze, and implement AI-driven learning analytics applications and tools within CSCL contexts.

计算机支持的协作学习中人工智能驱动的学习分析应用和工具:系统回顾
人工智能(AI)为在计算机支持的协作学习(CSCL)中实施学习分析带来了新的方法。然而,目前还缺乏以 CSCL 环境中人工智能驱动的学习分析应用和工具为重点的文献综述。为了填补这一空白,本系统性综述概述了现有人工智能驱动的学习分析应用和工具在CSCL中的目标、特点和效果。根据筛选标准,在 2004 年至 2023 年间初步确定的 2607 篇文章中,有 26 篇文章被纳入最终综述。我们的研究结果表明,现有工具主要关注学生的认知参与。现有工具主要利用交流话语、行为和评价数据来呈现结果和可视化效果。尽管现有工具提供了各种形式的反馈,但缺乏指导工具设计和开发过程的设计原则。此外,尽管人工智能技术已被用于呈现统计信息,但现有工具或应用程序中缺乏提供提醒或提示信息的功能。与对协作学习的积极影响相比,我们的研究结果表明,现有工具缺乏对教学干预的支持。本系统综述提出了以下理论、技术和实践意义:(1) 将教育和学习理论融入人工智能驱动的学习分析应用和工具;(2) 采用先进的人工智能技术收集、分析和解释多源和多模态数据;(3) 为教师提供可操作的建议和教学干预支持。基于我们的研究结果,我们就如何在 CSCL 环境中设计、分析和实施人工智能驱动的学习分析应用程序和工具提供了进一步的指导。
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
Educational Research Review
Educational Research Review EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
19.40
自引率
0.90%
发文量
53
审稿时长
57 days
期刊介绍: Educational Research Review is an international journal catering to researchers and diverse agencies keen on reviewing studies and theoretical papers in education at any level. The journal welcomes high-quality articles that address educational research problems through a review approach, encompassing thematic or methodological reviews and meta-analyses. With an inclusive scope, the journal does not limit itself to any specific age range and invites articles across various settings where learning and education take place, such as schools, corporate training, and both formal and informal educational environments.
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