SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. (March 2019)

• Record Type: Journal Article
• Authors: Gašević, Dragan; Joksimović, Srećko; Eagan, Brendan R.; Shaffer, David Williamson
• Is Part Of: Computers in human behavior. Volume 92(2019)
• Page Start: 562
• Page End: 577
• Publication Date: 2019-03
• Subjects: Social network analysis -- Epistemic network analysis -- Collaborative problem solving -- Learning analytics; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2018.07.003
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: In this paper, we propose a novel approach to the analysis of collaborative learning. The approach posits that different dimensions of collaborative learning emerging from social ties and content analysis of discourse can be modeled as networks. As such, the combination of social network analysis (SNA) and epistemic network analysis (ENA) analysis can detect information about a learner's enactment of what the literature on collaborative learning has described as a role: an ensemble of cognitive and social dimensions that is marked by interacting with the appropriate people about appropriate content. The proposed approach is named social epistemic network signature (SENS) and is defined as a combination of these two complementary network analytic techniques. The proposed SENS approach is examined on data produced in collaborative activities performed in a massive open online course (MOOC) delivered via a major MOOC platform. The results of a study conducted on a data set collected in a MOOC suggest SNA and ENA produce complementary results which can i) explain collaboration processes that shaped the creation of social ties and that were associated with different network roles; ii) describe differences between low and high performing groups of learners; and iii) show how combined properties derived from SNA and ENA predict academic performance. Highlights: A network analytics approach for collaborative learning is proposed. The approach combines social network analysis and epistemic network analysis. The approach is validated with a dataset from a massive open online course. Prediction of the structure of social ties and network roles with discourse is shown. Prediction of performance (groups) with discourse is demonstrated.