A review of methodological applications of social network analysis in computer-supported collaborative learning

Dado, Marielle; Bodemer, Daniel

doi:10.1016/j.edurev.2017.08.005

Cited by 114 publications

(108 citation statements)

References 60 publications

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“…Interactions among learners can be represented as a network, where each actor is represented as a node and the interaction as an edge. Results are usually compiled as an aggregated network of all of the interactions at a certain point in time, typically end of course [5,8,27]. Significant progress has been achieved by providing educators with tools and methods to visualize, quantify and mine relations and interactions, map collaborators roles, rank important collaborators and forecast students' future behavior or performance.…”

Section: Network Analysismentioning

confidence: 99%

High resolution temporal network analysis to understand and improve collaborative learning

Saqr

Nouri

2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

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There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.

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Section: Network Analysismentioning

confidence: 99%

High resolution temporal network analysis to understand and improve collaborative learning

Saqr

Nouri

2020

Proceedings of the Tenth International Conference on Learning Analytics &Amp; Knowledge

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“…At the time the original database searches were conducted in March 2017, we found one prior systematic literature review on SNA in e-learning (Cela et al, 2015) which mentions two other previous reviews (Sie et al, 2012;Zhao, Zhu, & Wu, 2011) on SNA. A rerun of the database searches in May 2018 identified another literature review (Dado & Bodemer, 2017) that examines trends in the application of SNA for investigating learner interactions in computersupported collaborative learning environments. None of the reviews make any mention of the CoP and/or CoI frameworks.…”

Section: Identifying the Need For A Systematic Reviewmentioning

confidence: 99%

Social Network Analysis and Online Learning Communities in Higher Education: A Systematic Literature Review

Jan

Vlachopoulos

Parsell

2019

OLJ

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This paper presents the results of a systematic literature review which sets out to explore the use of social network analysis (SNA) for investigating learning communities specifically, communities of practice (CoP) and community of inquiry (CoI) in higher education online learning (HEOL). The impetus for such a review originated from the reliance on extensive and time-consuming qualitative analysis typically required in research involving the CoP and CoI frameworks. The review consolidates and synthesizes existing research in HEOL in search of a methodological framework for structurally evaluating a CoP and/or CoI using SNA. We identified a handful of studies that integrate SNA measures and key structural components of the CoP and CoI frameworks and examined: SNA measures and corresponding theoretical components used; other analytical techniques used; limitations and; suggestions for further research. The selected studies reported disparate findings in terms of the relationship between SNA measures and the CoP and/or CoI components. The review also highlighted the need to complement SNA with a qualitative analytical technique. Therefore, whether SNA has the potential to be used as a stand-alone technique for structurally identifying communities remains to be seen. We also find a lack of consideration to attributional and performance variables in existing studies. In conclusion, we propose further research and the development of a fully integrated methodological framework which uses SNA to structurally evaluate a CoP and CoI.

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“…Through role-playing, learners will have a new understanding of the problem. The success of role playing can increase learners' sense of achievement and responsibility, and stimulate interest and enthusiasm in mastering knowledge [24]. For example, students play different roles, such as customers, designers, purchasers, implementers, etc.…”

Section: Role Assignmentmentioning

confidence: 99%

Design of Game-Based Collaborative Learning Model

Zhong¹

2019

JSS

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Collaborative learning mode has changed the traditional teaching mode, In the process of completing tasks, learners learn new knowledge points in the form of team cooperation, rather than relying on a large amount of knowledge inculcated by teachers, which helps to improve learners' thinking ability, communication ability and team cooperation ability. Game-based learning is a kind of instructional design which combines the design strategies of games. It enables learners to complete the learning content in a game-based way with a relaxed and pleasant environment. It helps to cultivate learners' initiative, creativity and cooperation in learning. Based on the above two concepts, this paper proposes a game-based cooperative learning teaching model, which has a clearer design and guidance for the teaching model. The learning effect of collaborative learning depends on the joint efforts of team members who have a strong positive dependence. Task-driven and appropriate division of labor can ensure that team members actively participate and share resources [3]. Rotation of roles can promote the development of students How to cite this paper: Zhong, Q. (2019) Design of Game-Based Collaborative Learning Model. Open Journal of Social Sciences, 7, 488-496.

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A review of methodological applications of social network analysis in computer-supported collaborative learning

Cited by 114 publications

References 60 publications

High resolution temporal network analysis to understand and improve collaborative learning

High resolution temporal network analysis to understand and improve collaborative learning

Social Network Analysis and Online Learning Communities in Higher Education: A Systematic Literature Review

Design of Game-Based Collaborative Learning Model

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