Detecting communities using social network analysis in online learning environments: Systematic literature review

Abstract

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. This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

Hierarchical Strategies for Building Multiethnic Communities from the Perspective of Data Mining Analysis

“…The research focuses on the single-ethnic research at the meso-level and the research on colleges and universities [11]. As far as the relevant research results have been found so far, few scholars have discussed the relevant issues of "forging the consciousness of the Chinese nation's community" from the perspective of the daily production and life practice of villagers (residents) within the "interembedded community" [12].…”

Lifelong and multirelational community detection to support social and collaborative e‐learning

“…Indeed, the value of the community internal density is important to more understand the evolution of each learners' network [24]. Furthermore, density is one of the most used measures as it reflects the frequency of information flow between learners which acts as an indicator of the network connectedness [56].…”

“…However, for other situations, a combined definition seems necessary. In addition, most of the proposed algorithms have considered the network of learners as static, which is not really the case [56]. Much more, the involvement of the dynamicity of the network makes it possible to find more lifelong and stable communities.…”

“…Furthermore, today a new challenge in social networks is that users are often connected by multiple types of social interactions simultaneously. Yet, all the different algorithms have ignored this important feature [56].…”

Mining Large Open Online Learning Networks: Exploring Community Dynamics and Communities of Performance

“…Social network analysis has been increasingly applied to the field of education (see review in Yassine et al, 2021). It includes but is not limited to: general engagement analysis for examining the emotional, cognitive, and behavioral interactions between learners and/or resources, behavior assessment which centers on diagnosing learner behaviors to identify any patterns of disorder that may need support and intervention, performance prediction to predict learners' achievement to aid in enhancing the teaching and learning processes, and recommender system development focusing on design filtration systems that provide personalized recommendations to assist learners based on their interest, preferences, and interaction patterns.…”