Learning with me Mate: Analytics of Social Networks in Higher Education
•Download as PPTX, PDF•
3 likes•3,336 views
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that - computational aspects of the study of social networks need to be integrated deeply with theory, research and practice, - novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and - innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (13)
Similar to Learning with me Mate: Analytics of Social Networks in Higher Education
Similar to Learning with me Mate: Analytics of Social Networks in Higher Education (20)
More from Dragan Gasevic
More from Dragan Gasevic (13)
Recently uploaded
Recently uploaded (20)
Learning with me Mate: Analytics of Social Networks in Higher Education
- 1. Learning with me mate Analytics of social networks in higher education Dragan Gasevic @dgasevic March 16, 2016 MCSHE, University of Melbourne Joint work with Srecko Joksimovic, Vitomir Kovanovic, and many great collaborators as cited in the presentation
- 2. Benefits of social learning
- 3. Social networks Ties as channels for flow of resources
- 4. The Strength of Weak Ties Connections through strong ties Connections through weak ties Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.
- 5. A common assumption Higher social network centrality leads to higher achievement Burt, R. S. (2000). The network structure of social capital. Research in organizational behavior, 22, 345-423.
- 9. Results in reality are inconsistent and contradictory
- 10. Network centrality and performance
- 11. What is the source of this inconsistency?
- 12. THEORY IN NETWORK ANALYSIS
- 14. Theory-informed learning analytics Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84.
- 15. Simmel’s theory of social interactions Networks based on super strong ties Triads as the unit of analysis
- 16. Study objective Network structural properties Learning outcome Social dynamic processes? Tie dynamics: • Homophily/ heterophily • Reciprocity • Triadic closure Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, K., de Kereki, I. F. (2016). Translating network position into performance: Importance of Centrality in Different Network Configurations. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (LAK 2016), Edinburgh, Scotland, UK (in press).
- 17. Method (Data) Code Yourself! (English), ¡A Programar! (Spanish) Certificate: 50% for the coursework; 75% - distinction 0 10000 20000 30000 40000 50000 60000 70000 Enrolled Engaged Engaged with forum Course participants Codeyourself Aprogramar 0 200 400 600 800 1000 1200 1400 1600 1800 Codeyourself Aprogramar Obtained certificate Normal Disctinction
- 19. Results - network characteristics -8 -6 -4 -2 0 2 4 6 Expansiveness Popularity Simmelian Reciprocity Gender Domestic Achievement (Normal) Achievement (None) Achievement (Distinct) Edges Aprogramar Codeyourself *** *** *** *** *** ** *** ** *** *** *** *** *** *** Note: * p<.05; ** p<.01; *** p<.001, Analysis of the estimates for the two ERG models
- 20. Results of the multinomial regression analysis, * p<.05; ** p<.01; *** p<.001 In order to provide meaningful visualizations, estimates for betweenness centrality were multiplied by 100 (only for the presentation purposes) -0.15 -0.1 -0.05 0 0.05 0.1 Betweenness (normal) Betweenness (distinct) Closeness (normal) Closeness (distinct) W. Degree (normal) W. Degree (distinct) Aprgoramar Codeyourself *** ** *** * ** *** *** Results – centrality vs. performance
- 21. “Super-strong” ties Social centrality does not necessarily imply benefits
- 22. Methodological implications Traditional (descriptive) + statistical network analysis
- 23. When and how are networks with super-strong ties formed?
- 25. Learning and discourse Graesser, A., Mcnamara, D., & Kulikowich, J. (2011). Coh-Metrix: Providing Multilevel Analyses of Text Characteristics. Educational Researcher, 40(5), 223–234. http://doi.org/10.3102/0013189X11413260
- 26. Language and social ties Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.
- 27. Interaction strategy, social networks, and performance Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., & Adesope, O. (2015). Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions. The Internet and Higher Education, 27, 74-89.
- 28. Method (data) Courses: Delft Design Approach (DDA), Introduction to Drinking Water (CTB), Functional Programming (FP) Certificate: 60% for the coursework 730 135 645 281 1064 1962 0 500 1000 1500 2000 2500 Engaged with forum Obtained certificate Forum participation & obtained certificates DDA CTB FP 11336 8484 316711397 1128 6560 0 10000 20000 30000 40000 50000 DDA CTB FP Students overview Enrolled Submitted Joksimović, S., Kovanović, V., Milikić, N., Jovanović, J., Gasević, D., Zouaq, A., Dawson, S. (2016). Effects of discourse on network formation and achievement in massive open online courses. Computers & Education (in preparation).
- 29. Discussion forum extract Weighted, directed graph Statistical network analysis Exponential random graph models Homophily Achievement Transition count Post count Reciprocity Popularity Expansiveness Simmelian ties
- 30. Discussion forum extract Weighted, directed graph Statistical network analysis Exponential random graph models Homophily Achievement Transition count Post count Reciprocity Popularity Expansiveness Simmelian ties student, post, timestamp post => keywords Alchemy API post_id, parent_post_id, student_id, keywords Block HMM Dominant topics Topic coherence Interpretation Paul, M. J. (2012). Mixed membership Markov models for unsupervised conversation modeling. In Proc. 2012 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 94-104).
- 31. Discussion forum extract Weighted, directed graph Statistical network analysis Exponential random graph models Homophily Achievement Transition count Post count Reciprocity Popularity Expansiveness Simmelian ties student, post, timestamp post => keywords Alchemy API post_id, parent_post_id, student_id, keywords Block HMM Dominant topics Topic coherence Association? Interpretation Regression analysis Interpretation Transition count Post count Replies count Betweenness centrality Closeness centrality Degree centrality
- 32. CTB DDA FP Results (topic transition)
- 33. Common ground as a key factor in shaping network structures Clark, H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington, DC, US: American Psychological Association.
- 34. The principle of least effort in communication Clark, H., & Krych, M. A. (2004). Speaking while Monitoring Addressees for Understanding. Journal of Memory and Language, 50(1), 62–81.
- 35. DDA topics Topic 11: Video concept - video making, - upload - particular assignment that included video making Topic 5: Course information - resources, - readings, - discussions Topic 7: Design thinking - thinking about design process, - different approaches to design
- 36. -8 -6 -4 -2 0 2 4 6 Expansiveness Popularity Assortative mixing Simmelian ties Simmelian cliques Reciprocity Post count Transition count Achievement Edges CTB DDA FP *** *** *** *** *** * *** Analysis of the estimates for the three ERG models Note: * p<.05; ** p<.01; *** p<.001 *** *** *** *** *** *** ** *** *** *** *** *** *** *** Results - network characteristics
- 37. Results (centrality vs. performance) -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 Betweenness Closeness W. Degree Post count Replies count Transition count CTB DDA FP R2 CTB = .17 R2 DDA = .21 R2 FP = .08 Results of the three regression analysis Note: * p<.05; ** p<.01; *** p<.001 *** *** * *** *** *** *** ***
- 38. FINAL REMARKS
- 39. One size fits all does not work in learning analytics Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
- 40. Theory as a driver of the study of networked learning
- 41. Interplay of language, network structure, and network dynamics
- 42. How to inform teaching practice?
- 43. Teaching to recognize structural wholes in networks Burt, R. S., & Ronchi, D. (2007). Teaching executives to see social capital: Results from a field experiment. Social Science Research, 36(3), 1156-1183.
- 44. Social presence in network formation Kovanovic, V., Joksimovic, S., Gasevic, D., & Hatala, M. (2014). What is the source of social capital? The association between social network position and social presence in communities of inquiry. Proceedings of 7th International Conference on Educational Data Mining – Workshops, London, UK, 2014
- 45. Scaling up qualitative research methods Kovanović, V., Joksimović, S., Waters, Z., Gašević, D., Kitto, K., Hatala, M., Siemens, G. (2016). Towards Automated Content Analysis of Discussion Transcripts: A Cognitive Presence Case In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (LAK 2016), Edinburgh, Scotland, UK (in press).
- 46. To what extent instructional design can affect network structures? Class size as an important factor Skrypnyk, O., Joksimović, S., Kovanović, V., Gašević, D., & Dawson, S. (2015). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. The International Review of Research in Open and Distributed Learning, 16(3).
- 47. Media, networks, and language
- 48. Personal agency and network structures
- 49. Adapting language to different situations
- 50. Tie building approach less important than experience in networks Burt, R. S., & Ronchi, D. (2007). Teaching executives to see social capital: Results from a field experiment. Social Science Research, 36(3), 1156-1183.
- 51. Ideally suited method Not ideally suited method Ideally suited method, but context dependent Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84-94. Capturing and measurement of engagement- related processes
- 52. Analytics-based feedback for networked learning
- 53. Thanks you!
Editor's Notes
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.