learning with me mate: analytics of social networks in higher education

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

Benefits of social learning

Social networks

Ties as channels for flow of resources

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.

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.

Network

Mike

Jill

Emma

Liz

Bob

Leah

ShaneJohn

Allen Lisa

Degree Centrality

Mike

Jill

Emma

Liz

Bob

Leah

ShaneJohn

Allen Lisa

Betweenness centrality

Mike

Jill

Emma

Liz

Bob

Leah

ShaneJohn

Allen Lisa

a.k.a. network broker

Results in reality are inconsistent and contradictory

Network centrality and performance

What is the source of this inconsistency?

THEORY IN NETWORK ANALYSIS

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.

Simmel’s theory of social interactions

Networks based on super strong ties

Triads as the unit of analysis

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).

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

Method (Analysis)

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

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Note: * p<.05; ** p<.01; *** p<.001, Analysis of the estimates for the two ERG models

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

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Results – centrality vs. performance

“Super-strong” ties

Social centrality does not necessarily imply benefits

Methodological implications

Traditional (descriptive) + statistical network analysis

When and how are networks with super-strong ties formed?

DISCOURSE IN NETWORK FORMATION

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

Language and social ties

Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380.

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.

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).

Discussion forum

extract

Weighted, directed graph

Statistical network analysis

Exponential random graph models Homophily

Achievement Transition count Post count

Reciprocity Popularity Expansiveness Simmelian ties

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).

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

CTB DDA FP

Results (topic transition)

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.

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.

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

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

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Analysis of the estimates for the three ERG modelsNote: * p<.05; ** p<.01; *** p<.001

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Results - network characteristics

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

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FINAL REMARKS

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.

Theory as a driver of the study of networked learning

Interplay of language, network structure, and network dynamics

How to inform teaching practice?

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.

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

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).

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).

Media, networks, and language

Personal agency and network structures

Adapting language to different situations

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.

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

Analytics-based feedback for networked learning

Thanks you!