E-learning occupies an increasingly prominent place in education. It provides the learner with a rich virtual network where he or she can exchange ideas and information and create synergies through interactions with other members of the network, whether fellow learners or teachers. Social network analysis (SNA) has proven extremely powerful at describing and analysing network behaviours in business, economics and medicine, but its application to e-learning has been relatively limited. This systematic review of the literature on SNA in e-learning aimed to assess the evidence for using SNA as a way to understand and improve e-learning systems, as well as suggest directions for future research. Most of the 37 studies included in this review applied various methods to analyse interaction patterns in forums involving one-mode networks. Indices of centrality and density were the SNA measures most often used. Although the small number of included studies means that our systematic review should be considered preliminary, the evidence so far strongly suggests that SNA, particularly when combined with content analysis, can provide a detailed understanding of the nature and type of interactions between members of the network, allowing for optimisation of course design, composition of learner groups and identification of learners in danger of dropping out. Future studies should examine two-mode networks and communication channels like chat rooms, wikis, blogs and microblogs. Whenever possible, future studies should also include a quantitative approach that exploits the statistical power of SNA to explain complex systems.

Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review.

As the field of learning analytics continues to mature, there is a corresponding evolution and sophistication of the associated analytical methods and techniques. In this regard social network analysis (SNA) has emerged as one of the cornerstones of learning analytics methodologies. However, despite the noted importance of social networks for facilitating the learning process, it remains unclear how and to what extent such network measures are associated with specific learning outcomes. Motivated by Simmel's theory of social interactions and building on the argument that social centrality does not always imply benefits, this study aimed to further contribute to the understanding of the association between students' social centrality and their academic performance. The study reveals that learning analytics research drawing on SNA should incorporate both - descriptive and statistical methods to provide a more comprehensive and holistic understanding of a students' network position. In so doing researchers can undertake more nuanced and contextually salient inferences about learning in network settings. Specifically, we show how differences in the factors framing students' interactions within two instances of a MOOC affect the association between the three social network centrality measures (i.e., degree, closeness, and betweenness) and the final course outcome.

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Translating network position into performance: importance of centrality in different network configurations

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

Instructors' discussion design and facilitation have critical influences on online learning community development. Emerging network analysis methods were used to examine the development of an online learning community within a graduate-level course, the variations of an experienced instructor's discussion design, and the dynamics of her discussion facilitation. Results indicated that students gradually formed an interactive online learning community. The instructor, overall, played a facilitator role in this community; yet her participatory roles varied within different discussions during different time frames. Her participatory role evolved from a guide in the first class discussion, to varying roles, i.e., a facilitator, an observer, and a collaborator within different group discussions at the middle stages of the course, and to an observer in the course's later stages. Methodological implications for using social network analysis in online learning community research, and practical implications for designing and facilitating discussions that foster online learning communities were proposed.

The influences of an experienced instructor's discussion design and facilitation on an online learning community development: A social network analysis study

The growth of online learning environments entails understanding of how to promote collaborative knowledge construction processes and create learning environments that support meaningful student engagement and interactions. Asynchronous online discussion forums are intended to support knowledge construction and higher-order thinking and are becoming even more appealing for their predictive relationship with learning. This paper describes a study that explored the complex dynamics of knowledge construction in pre-service teacher education through examining student teachers' cognitive presence in online discussion forums and their academic performance. The results of multiple regression analysis showed that certain levels of cognitive presence were associated with students' academic performance. In addition, network analysis of discussion forums revealed that student centrality within their learning networks moderated the association between the highest level of cognitive presence and academic performance. The paper concludes with discussing the theoretical and practical implications of the findings.

Students' engagement in asynchronous online discussion: The relationship between cognitive presence, learner prominence, and academic performance

By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided.

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Social network analysis for technology-enhanced learning: review and future directions

In e-learning settings, the interactions of students with one another, with the course content and with the instructors generate a considerable amount of information that may be useful for understanding how people learn online. The objective of the present research was to use social network analysis to explore the social structure of an e-learning environment and analyse how the structure depends on user learning style. Applying Felder and Silverman's inventory of learning styles to an online course with 214 adult graduate-level participants, we found that some styles occupied more central positions than others. These findings may help designers improve learning activities by tailoring them to the learning styles of the participants. [ABSTRACT FROM AUTHOR]

Influence of learning styles on social structures in online learning environments

Introduction Designed to mimic the fact that collaboration is an everyday activity, the collaboration approach has been widely used since the 1970s (Strijbos, Martens, & Jochems, 2004), and it has taken on particular prominence in education within the last decade (Laal & Ghodsi, 2012). eLearning environments typically incorporate several tools and communication channels to support collaboration among learners (Koschmann, Kelson, Feltovich, & Barrows, 1996). The term "collaboration" has been widely applied and studied in many fields with the aim of optimising and exploiting the synergies made possible by collective intelligence (Levy, 2007). Collaboration supports corporate success by fostering the transfer of knowledge within multidisciplinary teams (Skyrme, 2013). Olson, Olson, Carter, and Storrosten (1992) analysed how different members of one organisation collaborated during meetings, and they found that a significant amount of time was spent designing the format of the discussion. Robson and Bennett (2000) found that collaborative modes of interaction between small businesses and their suppliers were critical to increasing turnover. Collaboration in education has been widely studied (Dillenbourg, Baker, Blaye, & O'Malley, 1995). In one seminal study, Dewiyanti, Brand-Gruwel, Jochems, and Broers (2007) found that group satisfaction with collaborative work varied directly with group cohesion, and that most learners enjoyed working on collaborative projects. So and Brush (2008) found that students who participated more in collaborative learning projects were more satisfied with the learning experience than learners who participated less. Educational technology in general and eLearning in particular exploit a range of platforms and communication tools to harness the learning power of collaboration. Evidence suggests that 3D immersive environments, in which learners interact in a virtual learning world, are particularly effective at catalysing collaboration among learners (Lorenzo, Sicilia & Sanchez, 2012). One of the key questions when designing collaborative tasks is how to assign learners to collaborative groups. This is particularly important when the target audience is adult learners, since they often exhibit strong preferences about what topics they wish to learn and which learning strategies and tactics they wish to use (Knowles, 1970). Thus designing collaborative tasks according to learner's preferences may provide strategic insights into instructional design for adult learners in on-line courses. Surprisingly, little is known about how learner's preferences influence collaborative learning outcomes in eLearning. The present study adopts the relatively new and underexploited approach of combining content analysis (CA) and Social Network Analysis (SNA) to examine the effects of learner preferences on participation in collaborative projects and on learning performance. Here we compare collaboration in quantitative and qualitative terms and in terms of learning performance between groups of on-line learners organised randomly and groups containing only members interested in the assigned topic. We wanted to know whether learners who work only on their preferred topics collaborate differently than learners who are assigned topics randomly. To gain insights into how learners collaborate under each type of group composition, we took a combined approach of SNA and CA. These complementary techniques provide insights into, respectively, the quantity and quality of interaction (Erlin, Yusof, & Rahman, 2008; Erlin, Yusof, & Rahman, 2009; Rabbany k., Takaffoli, & Zaiane, 2012). Theoretical background Collaborative learning Collaborative learning is an instructional approach in which students work in groups to solve a problem (Dillenbourg & Schneider, 1995). In learning environments, collaboration can promote critical thinking (Gokhale, 1995), lead to better learning outcomes than traditional methods under certain conditions (Terenzini, Cabrera, Colbeck, Parente, & Bjorklund, 2001), develop social interaction skills and help students develop a sense of responsibility towards one another (Laal & Ghodsi, 2012), and develop social competencies (Bruffee, 1995) cognitive skills (Le, 2002) and problem-solving abilities (Blaye et al. …

Karina L. Cela

Papers

Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review.

Social network analysis for technology-enhanced learning: review and future directions

Influence of learning styles on social structures in online learning environments

Comparison of Collaboration and Performance in Groups of Learners Assembled Randomly or Based on Learners' Topic Preferences