An automated analysis of topic distributions and features approach to promoting group performance, collaborative knowledge building and socially shared regulation in online collaborative learning

IF 3.3 3区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Lanqin Zheng, Lu Zhong, Yunchao Fan
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引用次数: 0

Abstract

Online collaborative learning has been widely used in the field of education. However, unrelated or off-topic information is often included in online collaborative learning. Furthermore, the content of online discussion is often too shallow or narrow. To achieve productive collaborative learning, this study proposed and validated an automated analysis of topic distributions and features (AATDF) approach. In total, 189 college students in China participated in this study and were assigned to one of two experimental groups or a control group. Experimental Group 1 participated in online collaborative learning with the AATDF approach. Experimental Group 2 participated in online collaborative learning with the automated analysis of topic distributions (AATD) approach. The control group participated in traditional online collaborative learning without any specified approach. The results indicate that the AATDF approach can significantly promote group performance, collaborative knowledge building and socially shared regulation compared with the AATD and traditional online collaborative learning approaches. The results and implications are also discussed in depth. The main contribution of this study is that the AATDF approach can improve learning performance and bring online collaborative learning onto new ground. Implications for practice: The AATDF approach is very useful and effective for promoting group performance, collaborative knowledge building and socially shared regulation. Teachers and practitioners can provide personalised interventions and optimise collaborative learning design based on the analysis results of topic distributions and features. Developers can adopt deep neural network models to develop intelligent online
在线协作学习中促进小组绩效、协作知识建设和社会共享规则的主题分布和特征自动分析方法
在线协作学习在教育领域得到了广泛的应用。然而,在线协作学习中经常包含不相关或离题的信息。此外,网上讨论的内容往往过于肤浅或狭隘。为了实现高效的协作学习,本研究提出并验证了主题分布和特征的自动分析(AATDF)方法。中国共有189名大学生参与了这项研究,他们被分为两个实验组和对照组。实验组1采用AATDF方式参与在线协作学习。实验组2采用主题分布自动分析(automated analysis of topic distribution, AATD)方法进行在线协同学习。对照组参加传统的在线协作学习,没有任何特定的方法。结果表明,与传统的在线协作学习方式相比,AATDF学习方式可以显著促进团队绩效、协作知识建设和社会共享监管。本文还对研究结果及其意义进行了深入讨论。本研究的主要贡献在于,AATDF方法可以提高学习绩效,并将在线协作学习带入新的领域。对实践的启示:AATDF方法在促进团队绩效、协作知识建设和社会共享监管方面非常有用和有效。教师和实践者可以根据主题分布和特征的分析结果,提供个性化干预,优化协同学习设计。开发者可以采用深度神经网络模型进行智能在线开发
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来源期刊
Australasian Journal of Educational Technology
Australasian Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
7.60
自引率
7.30%
发文量
54
审稿时长
36 weeks
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