Learning with me Mate: Analytics of Social Networks in Higher Education
-
Upload
dragan-gasevic -
Category
Education
-
view
1.932 -
download
4
Transcript of Learning with me Mate: Analytics of Social Networks in Higher Education
![Page 1: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/1.jpg)
Learning with me mateAnalytics of social networks in higher education
Dragan Gasevic@dgasevic
March 16, 2016MCSHE, University of Melbourne
Joint work with Srecko Joksimovic, Vitomir Kovanovic, and many great collaborators as cited in the presentation
![Page 2: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/2.jpg)
Benefits of social learning
![Page 3: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/3.jpg)
Social networks
Ties as channels for flow of resources
![Page 4: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/4.jpg)
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.
![Page 5: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/5.jpg)
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.
![Page 6: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/6.jpg)
Network
Mike
Jill
Emma
Liz
Bob
Leah
ShaneJohn
Allen Lisa
![Page 7: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/7.jpg)
Degree Centrality
Mike
Jill
Emma
Liz
Bob
Leah
ShaneJohn
Allen Lisa
![Page 8: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/8.jpg)
Betweenness centrality
Mike
Jill
Emma
Liz
Bob
Leah
ShaneJohn
Allen Lisa
a.k.a. network broker
![Page 9: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/9.jpg)
Results in reality are inconsistent and contradictory
![Page 10: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/10.jpg)
Network centrality and performance
![Page 11: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/11.jpg)
What is the source of this inconsistency?
![Page 12: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/12.jpg)
THEORY IN NETWORK ANALYSIS
![Page 13: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/13.jpg)
![Page 14: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/14.jpg)
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.
![Page 15: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/15.jpg)
Simmel’s theory of social interactions
Networks based on super strong ties
Triads as the unit of analysis
![Page 16: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/16.jpg)
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).
![Page 17: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/17.jpg)
Method (Data) Code Yourself! (English), ¡A Programar! (Spanish)
Certificate: 50% for the coursework; 75% - distinction
Enrolled Engaged Engaged with forum
010000200003000040000500006000070000
Course participants
Codeyourself Aprogramar
Codeyourself Aprogramar0
200400600800
10001200140016001800
Obtained certificate
Normal Disctinction
![Page 18: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/18.jpg)
Method (Analysis)
![Page 19: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/19.jpg)
Results - network characteristics
Expansiveness
Popularity
Simmelian
Reciprocity
Gender
Domestic
Achievement (Normal)
Achievement (None)
Achievement (Distinct)
Edges
-8 -6 -4 -2 0 2 4 6
Aprogramar Codeyourself
******
******
*****
*****
***
******
***
******
Note: * p<.05; ** p<.01; *** p<.001, Analysis of the estimates for the two ERG models
![Page 20: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/20.jpg)
Results of the multinomial regression analysis, * p<.05; ** p<.01; *** p<.001In order to provide meaningful visualizations, estimates for betweenness centrality were multiplied by 100 (only for the presentation purposes)
Betweenness (normal)
Betweenness (distinct)
Closeness (normal)
Closeness (distinct)
W. Degree (normal)
W. Degree (distinct)
-0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08
Aprgoramar Codeyourself
*****
***
*
**
***
***
Results – centrality vs. performance
![Page 21: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/21.jpg)
“Super-strong” ties
Social centrality does not necessarily imply benefits
![Page 22: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/22.jpg)
Methodological implications
Traditional (descriptive) + statistical network analysis
![Page 23: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/23.jpg)
When and how are networks with super-strong ties formed?
![Page 24: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/24.jpg)
DISCOURSE IN NETWORK FORMATION
![Page 25: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/25.jpg)
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
![Page 26: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/26.jpg)
Language and social ties
Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.
![Page 27: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/27.jpg)
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.
![Page 28: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/28.jpg)
Method (data)
Courses: Delft Design Approach (DDA), Introduction to Drinking Water (CTB), Functional Programming (FP)
Certificate: 60% for the coursework
Engaged with forum Obtained certificate0
500
1000
1500
2000
2500
730
135
645281
1064
1962
Forum participation & obtained certificates
DDA CTB FP
DDA CTB FP0
50001000015000200002500030000350004000045000
11336 8484
3167113971128
6560
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).
![Page 29: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/29.jpg)
Discussion forum
extract
Weighted, directed graph
Statistical network analysis
Exponential random graph models Homophily
Achievement Transition count Post count
Reciprocity Popularity Expansiveness Simmelian ties
![Page 30: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/30.jpg)
Discussion forum
extract
Weighted, directed graph
Statistical network analysis
Exponential random graph models Homophily
Achievement Transition count Post count
Reciprocity Popularity Expansiveness Simmelian ties
extra
ctstudent, post, timestamp
post => keywords Alchemy API
post_id, parent_post_id, student_id, keywordsBlock 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).
![Page 31: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/31.jpg)
Discussion forum
extract
Weighted, directed graph
Statistical network analysis
Exponential random graph models Homophily
Achievement Transition count Post count
Reciprocity Popularity Expansiveness Simmelian ties
extra
ctstudent, post, timestamp
post => keywords Alchemy API
post_id, parent_post_id, student_id, keywordsBlock HMM
Dominant topics Topic coherence
Association?Interpretation
Regression analysis
Interpretation
Transition count Post count Replies count Betweenness centrality Closeness centrality Degree centrality
![Page 32: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/32.jpg)
CTB DDA FP
Results (topic transition)
![Page 33: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/33.jpg)
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.
![Page 34: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/34.jpg)
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.
![Page 35: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/35.jpg)
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
![Page 36: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/36.jpg)
Expansiveness
Popularity
Assortative mixing
Simmelian ties
Simmelian cliques
Reciprocity
Post count
Transition count
Achievement
Edges
-8 -6 -4 -2 0 2 4 6
CTB DDA FP
******
***
***
***
****
Analysis of the estimates for the three ERG modelsNote: * p<.05; ** p<.01; *** p<.001
*** ***
***
***
***
***
**
***
***
***
***
***
***
***
Results - network characteristics
![Page 37: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/37.jpg)
Results(centrality vs. performance)
Betweenness
Closeness
W. Degree
Post count
Replies count
Transition count
-0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12
CTB DDA FP
R2CTB = .17
R2DDA = .21
R2FP = .08
Results of the three regression analysisNote: * p<.05; ** p<.01; *** p<.001
***
***
*
***
******
***
***
![Page 38: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/38.jpg)
FINAL REMARKS
![Page 39: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/39.jpg)
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.
![Page 40: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/40.jpg)
Theory as a driver of the study of networked learning
![Page 41: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/41.jpg)
Interplay of language, network structure, and network dynamics
![Page 42: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/42.jpg)
How to inform teaching practice?
![Page 43: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/43.jpg)
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.
![Page 44: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/44.jpg)
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
![Page 45: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/45.jpg)
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).
![Page 46: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/46.jpg)
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).
![Page 47: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/47.jpg)
Media, networks, and language
![Page 48: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/48.jpg)
Personal agency and network structures
![Page 49: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/49.jpg)
Adapting language to different situations
![Page 50: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/50.jpg)
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.
![Page 51: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/51.jpg)
Ideally suited methodNot ideally suited methodIdeally 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
![Page 52: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/52.jpg)
Analytics-based feedback for networked learning
![Page 53: Learning with me Mate: Analytics of Social Networks in Higher Education](https://reader035.fdocuments.us/reader035/viewer/2022062522/588304551a28abe70d8b618b/html5/thumbnails/53.jpg)
Thanks you!