Detecting communities using social network analysis in online learning environments: Systematic literature review,WIREs Data Mining and Knowledge Discovery

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Detecting communities using social network analysis in online learning environments: Systematic literature review
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-09-25 , DOI: 10.1002/widm.1431
Sahar Yassine ₁ , Seifedine Kadry ₂ , Miguel‐Angel Sicilia ₁

Affiliation

Uncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities.

中文翻译：

在在线学习环境中使用社交网络分析检测社区：系统文献回顾

揭示社区结构在解释、分析和预测与社会学、犯罪学、生物学、医学、传播学、经济学和学术界不同领域相关的网络行为和动态方面取得了重大进展。检测和聚类社区是识别社交网络中的结构属性和行为模式的有力步骤。最近，在线学习已逐渐被许多教育实践所采用，这引发了许多关于评估学习者在新兴学习社区中的参与、协作和行为的问题。这篇系统的文献综述旨在评估社区检测技术在分析在线学习环境中的网络结构中的使用。它全面概述了采用这些技术的现有研究，确定了其应用背后的教育目标，并提出了未来可能的研究方向。我们的分析涵盖了文献中发现的 65 项研究，并将不同的社区发现技术应用于各种类型的在线学习环境，以分析其用户的交互模式。我们的审查揭示了该领域在改进教育实践和决策以及利用与这些环境交互产生的大量数据方面的潜力。最后，

更新日期：2021-09-25

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