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

Karina, L.; Sicilia, Miguel Ángel; Sánchez, Salvador

doi:10.1007/s10648-014-9276-0

Cited by 156 publications

(136 citation statements)

References 60 publications

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“…Prior studies of Social Network Analysis (SNA) in e-learning, particularly in the improvement of LD have concentrated on examining patterns of learner communication and collaboration in various situations, such as when discussing, blogging and e-mailing [31]. Within the last three years in LA, SNA has been shown to be an effective tool to explore the relationships of learners in online discussion forum [29], as well as in face-to-face interactions, tracked for instance in eye tracking movements [32].…”

Section: Discussionmentioning

confidence: 99%

Mixing and Matching Learning Design and Learning Analytics

Nguyen¹,

Rienties²,

Toetenel³

2017

Learning and Collaboration Technologies. Technology in Education

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Abstract. In the last five years, learning analytics has proved its potential in predicting academic performance based on trace data of learning activities. However, the role of pedagogical context in learning analytics has not been fully understood. To date, it has been difficult to quantify learning in a way that can be measured and compared. By coding the design of e-learning courses, this study demonstrates how learning design is being implemented on a large scale at the Open University UK, and how learning analytics could support as well as benefit from learning design. Building on our previous work, our analysis was conducted longitudinally on 23 undergraduate distance learning modules and their 40,083 students. The innovative aspect of this study is the availability of fine-grained learning design data at individual task level, which allows us to consider the connections between learning activities, and the media used to produce the activities. Using a combination of visualizations and social network analysis, our findings revealed a diversity in how learning activities were designed within and between disciplines as well as individual learning activities. By reflecting on the learning design in an explicit manner, educators are empowered to compare and contrast their design using their own institutional data.

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Section: Discussionmentioning

confidence: 99%

Mixing and Matching Learning Design and Learning Analytics

Nguyen¹,

Rienties²,

Toetenel³

2017

Learning and Collaboration Technologies. Technology in Education

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“…Prior studies of Social Network Analysis (SNA) in e-learning, particularly in the improvement of LD have concentrated on examining patterns of learner communication and collaboration in various situations, such as when discussing, blogging and e-mailing [8]. Within the last three years in LA, SNA has been shown to be an effective tool to explore the relationships of learners in online discussion forum [9,23,25,27,41], or in eye tracking movements [52].…”

Section: Research Questionsmentioning

confidence: 99%

Unravelling the dynamics of instructional practice

Nguyen

Rienties

Toetenel

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

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Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics.

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“…Finally, as informal and formal relations influence the teaching support received by participants, academic development programmes should encourage participants to reflect on, explore and perhaps cultivate relationships that can positively affect or shape their teaching practices. Further, while in the past the technical barriers for adoption of SNA techniques and methods were substantial (Cela et al, 2014;Katz et al, 2004), with increased availability of (almost) free software packages (e.g., Gephi, UCINET), relatively simple to use Windows-based menu structures, substantial support materials and this special issue in particular, the timing seems to right for academic developers to explore the affordances of SNA techniques.…”

Section: Discussionmentioning

confidence: 99%

“…However, there are few studies on how participants engage in dialogue with each other, socially co-construct and share knowledge together beyond the formal AD programme environment (Cornelissen et al, 2014;Van Waes, Van de Bossche, Moolenaar, De Maeyer, & Van Petegem, Forthcoming). It is necessary to unpack how participants in AD programmes learn from the experiences of each other, as research has highlighted that social network developments influence learning processes and learning outcomes (Cela, Sicilia, & Sánchez, 2014;Hommes et al, 2012;Katz, Lazer, Arrow, & Contractor, 2004). How academics develop social network relations and engage in general with the AD programme may be dependent on the design of the academic development programme (Clarke & Reid, 2012;Ziegenfuss & Lawler, 2008), the "external" network of participants (Akkerman & Bakker, 2011;McCormick, Fox, Carmichael, & Procter, 2010), and/or the organisational cultures within the participants' departments (Daly & Finnigan, 2010;de Lima, 2007).…”

Section: Introductionmentioning

confidence: 99%