Learning with me Mate: Analytics of Social Networks in Higher Education

Editor's Notes

• #3: 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.
• #4: 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.
• #10: 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.
• #12: 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.
• #16: Benefits of network centrality are possible in networks with weak ties Network centrality does (not) necessarily imply less constraints and more benefit otherwise Simmel’s theory of social interactions allows for investigation whether networks are based on super strong ties. This can be analyzed with statistical methods for social network analysis (SNA) such as exponential random growth models (ERGMs) that allow for the use of triads as units of analysis, unlike commonly used dyads in descriptive SNA
• #20: This diagram shows the results of the analysis conducted with the ERG models. It shows a significant effect of homphilic relationships between students (e.g., those who are similar in terms of achievement, country of origin) are more likely to connect. Likewise, there is a high level of reciprocity in both networks (e.g., if student A ask something student B, student B replied back to student A). The most striking results was that the social network in CodeYourself! was based on Simmelian ties (i.e., super strong ties), while this was not the case for the social network in Aprogramar. Therefore, theoretically, we expected that network centrality would be a significant predictor of achievement (i.e., grades and completion) in Aprogramar as its network was based on weak ties, while this was not the case for CodeYourself due to the nature of its network.
• #21: The hypothesized statements from the previous slide were confirmed: network centrality was a significant predictor of achievement (i.e., grades and completion) in Aprogramar as its network was based on weak ties, while this was not the case for CodeYourself due to the nature of its network.
• #26: Psychological models of discourse comprehension and learning, such as the construction-integration, constructionist, and indexical-embodiment models, lend themselves nicely to the exploration of learning related phenomena in computer-mediated educational environments. These psychological frameworks have identified the representations, structures, strategies, and processes at multiple levels of discourse (Graesser & McNamara, 2011; Kintsch, 1998; Snow, 2002). Five levels have frequently been identified in these frameworks: (1) words, (2) syntax, (3) the explicit textbase, (4) the situation model (sometimes called the mental model), and (5) the discourse genre and rhetorical structure (the type of discourse and its composition). The computational linguistic facility used in the correct study, Coh-Metrix (described more in the methods), allows us to capture these main levels of discourse. In the learning context, learners can experience communication misalignments and comprehension breakdowns at different levels. Such breakdowns and misalignments have important implications for the learning process.
• #27: Language is a primary means for expressing and exchanging content through a network. It is through language that participants are able to build connections and define social ties with other actors. With regard to analytical approaches, there has been extensive knowledge gleaned from manual content analyses of learners’ discourse during educational interactions. For instance, the early research of Bernstein (1971) highlighted that individuals with more complex social networks tend to demonstrate more formal and elaborated speech forms than those with more simple and densely connected personal networks. Milroy and Margrain (1980) reported that the variety of language in use is dependent on the density of the social network and the multiplexity of the ties. According to Granovetter (1973), the intensity of ties established between actors affords an opportunity to track the linguistic phenomenon of code-switching, whereby speakers change conversational styles as they converse with interlocutors from the different parts of their sub-networks. These earlier studies illustrate the relationship between social ties and language. However, the manual content analysis methods used in those studies are no longer a viable option with the increasing scale of educational data. Consequently, researchers have been incorporating automated linguistic analysis that range from shallow level word counts to deeper level discourse analysis.
• #45: Talk about social presence – affective, interactive and cohesive and indicating that interactive is typically always playing the role, while affective plays in betweenness, while cohesive plays in degree as well. Also indicated that affective emerges as a separate construct within social presence scales.
• #46: Talk about social presence – affective, interactive and cohesive and indicating that interactive is typically always playing the role, while affective plays in betweenness, while cohesive plays in degree as well. Also indicated that affective emerges as a separate construct within social presence scales.