Anatomizing online collaborative inquiry using directional epistemic network analysis and trajectory tracking

IF 6.7 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Shen Ba, Xiao Hu, David Stein, Qingtang Liu
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引用次数: 0

Abstract

Accurate assessment and effective feedback are crucial for cultivating learners' abilities of collaborative problem-solving and critical thinking in online inquiry-based discussions. Based on quantitative content analysis (QCA), there has been a methodological evolvement from descriptive statistics to sequential mining and to network analysis for mining coded discourse data. Epistemic network analysis (ENA) has recently gained increasing recognition for modelling and visualizing the temporal characteristics of online discussions. However, due to methodological restraints, some valuable information regarding online discussion dynamics remains unexplained, including the directionality of connections between theoretical indicators and the trajectory of thinking development. Guided by the community of inquiry (CoI) model, this study extended generic ENA by incorporating directional connections and stanza-based trajectory tracking. By examining the proposed extensions with discussion data of an online learning course, this study first verified that the extensions are comparable with QCA, indicating acceptable assessment validity. Then, the directional ENA revealed that two-way connections between CoI indicators could vary over time and across groups, reflecting different discussion strategies. Furthermore, trajectory tracking effectively detected and visualized the fine-grained progression of thinking. At the end, we summarize several research and practical implications of the ENA extensions for assessing the learning process.

Abstract Image

利用定向认识论网络分析和轨迹跟踪剖析在线协作探究
在基于探究的在线讨论中,准确的评估和有效的反馈对于培养学习者合作解决问题和批判性思维的能力至关重要。在定量内容分析(QCA)的基础上,挖掘编码话语数据的方法已经从描述性统计发展到顺序挖掘和网络分析。最近,认识论网络分析(ENA)在模拟和可视化在线讨论的时间特征方面获得了越来越多的认可。然而,由于方法上的限制,有关在线讨论动态的一些有价值的信息仍未得到解释,包括理论指标和思维发展轨迹之间联系的方向性。在探究社区(CoI)模型的指导下,本研究通过纳入方向性联系和基于章节的轨迹跟踪,对通用ENA进行了扩展。通过使用在线学习课程的讨论数据对所提出的扩展进行检验,本研究首先验证了扩展与 QCA 的可比性,表明评估有效性是可以接受的。然后,定向ENA揭示了CoI指标之间的双向联系会随着时间和小组的不同而变化,反映了不同的讨论策略。此外,轨迹跟踪能有效地检测和直观显示思维的细粒度进展。最后,我们总结了ENA扩展在评估学习过程方面的一些研究和实践意义。 实践者注释关于本主题的已知内容 评估和反馈对于在基于探究的在线讨论中培养合作解决问题和批判性思维至关重要。认知存在是描述在线探究式讨论中思维进展的一个重要结构。认识论网络分析在模拟在线探究的时间特征方面日益得到认可。本文的补充论述之间的定向连接可以反映出群体和个人不同的在线讨论策略。用 "探究社区 "模型对一对相连的论述进行编码,可以根据它们的时间顺序产生不同的含义。轨迹跟踪方法可以揭示在线探究式讨论中的细微思维进展。对实践和/或政策的启示 除了单个论述的出现,研究在线讨论中论述的方向性共现的意义也很有价值。小组和个人可以采用不同的讨论策略,并遵循不同的思维发展路径。发展评估对于了解参与者如何取得特定成果和提供适应性反馈至关重要。
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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