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

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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.

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

  1. 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. 2. Benefits of social learning
  3. 3. Social networks Ties as channels for flow of resources
  4. 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. 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.
  6. 6. Network Mike Jill Emma Liz Bob Leah ShaneJohn Allen Lisa
  7. 7. Degree Centrality Mike Jill Emma Liz Bob Leah ShaneJohn Allen Lisa
  8. 8. Betweenness centrality Mike Jill Emma Liz Bob Leah ShaneJohn Allen Lisa a.k.a. network broker
  9. 9. Results in reality are inconsistent and contradictory
  10. 10. Network centrality and performance
  11. 11. What is the source of this inconsistency?
  12. 12. THEORY IN NETWORK ANALYSIS
  13. 13. 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.
  14. 14. Simmel’s theory of social interactions  Networks based on super strong ties  Triads as the unit of analysis
  15. 15. 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).
  16. 16. 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
  17. 17. Method (Analysis)
  18. 18. 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
  19. 19. 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
  20. 20. “Super-strong” ties Social centrality does not necessarily imply benefits
  21. 21. Methodological implications Traditional (descriptive) + statistical network analysis
  22. 22. When and how are networks with super-strong ties formed?
  23. 23. DISCOURSE IN NETWORK FORMATION
  24. 24. 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
  25. 25. Language and social ties Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.
  26. 26. 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.
  27. 27. 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).
  28. 28. Discussion forum extract Weighted, directed graph Statistical network analysis  Exponential random graph models  Homophily  Achievement  Transition count  Post count  Reciprocity  Popularity  Expansiveness  Simmelian ties
  29. 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 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).
  30. 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 Association? Interpretation Regression analysis Interpretation  Transition count  Post count  Replies count  Betweenness centrality  Closeness centrality  Degree centrality
  31. 31. CTB DDA FP Results (topic transition)
  32. 32. 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.
  33. 33. 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.
  34. 34. 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
  35. 35. -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
  36. 36. 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 *** *** * *** *** *** *** ***
  37. 37. FINAL REMARKS
  38. 38. 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.
  39. 39. Theory as a driver of the study of networked learning
  40. 40. Interplay of language, network structure, and network dynamics
  41. 41. How to inform teaching practice?
  42. 42. 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.
  43. 43. 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
  44. 44. 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).
  45. 45. 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).
  46. 46. Media, networks, and language
  47. 47. Personal agency and network structures
  48. 48. Adapting language to different situations
  49. 49. 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.
  50. 50. 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
  51. 51. Analytics-based feedback for networked learning
  52. 52. Thanks you!

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