Presentation for Sunbelt XXXV, Brighton, United Kingdom. This version misses three videos.

1. Exploring classroom
interaction with dynamic social
network analysis
Dr. Christian Bokhove
University of Southampton
SUNBELT XXXV
26th June 2015

2. Rationale
• Dynamic model (Creemers & Kyriakides, 2008)
– Multilevel: students in classrooms in schools
– Classroom interaction
• Social networks
– Actors and interactions
– Multidisciplinary (Freeman, 2004)

3. Classroom observation
• Review classroom dialogue Howe and Abedin (2014)
– Quantitative vs Qualitative
• TIMSS (Trends in International Mathematics and Science Study)
video study (Hiebert et al., 1999)
– Video observations
– National patterns of teaching (Givvin, Hiebert, Jacobs,
Hollingsworth, & Gallimore, 2005)
• Lesson signatures
3

4. SNA for classroom interaction
• Case to use SNA for
classroom interaction
• Making it dynamic
– Classroom interaction
(Moody, McFarland,
& Bender-deMoll, 2005)
• Technological and methodological advances
– Observation apps
– Video recording easier
– Statistical techniques and packages to capture
temporal aspects like Gephi, ERGMs, Rsiena,
Statnet, Relevent

5. This project
• Use dynamic social network analysis to
describe classroom interaction
• Data analysis and visualization software
– Gephi 0.8.2 beta
– R and Rstudio with the packages statnet
(Handcock, Hunter, Butts, Goodreau, &
Morris, 2008) and ndtv (Bender-deMoll, 2014)

6. Observation apps

7. Data analyses
• Three data analyses approaches
– A: transcripts of TIMSS used ‘as is’ because
low effort with existing transcripts  Gephi
– B: TIMSS videos re-observed
to get more detail  Gephi, Rstudio (statnet
and ndtv)
– C: Five observation of maths lessons in a
secondary school in the south of the United
Kingdom  Using Lesson App, Gephi (incl.
animations)

8. Data preparation
8

9. Two TIMSS lessons: US1 and HK1
• US1
– USA 8th grade
– Maths: graphing linear equations
– 44m, 36 students, mainly self work and private
interaction
• HK1
– Hong Kong SAR 8th grade
– Maths: square numbers and roots
– 34m, 40 students, whole class first then exercises
9

10. Results – analysis B
HK1 US1
Nodes 44 35
Edges 51 95
Average degree 1.159 2.714
Average weighted degree 3.273 21.4129
Duration of interaction

11. Analysis (US1 only) - ndtv

12. Results – analysis C
Lesson R1 Lesson R4
Topic
Proportions Area of triangles
Year
Year 10 Year 7
Visualisation
Nodes (*)
16 25
Edges (**)
33 75
Degree
The size of the nodes indicates the total degree
Average degree
2.062 3.0
Av.clust.coeff.
0.334 0.322 12

13. 13

14. Metrics over time
14

15. What might it tell us?
• Teacher student interaction
– Whole class, directionality
• Student interactions
– Groups and cliques
• Individual behaviour
– Help seeking
– Disturbances
– Central students
• Perhaps, patterns over classes, schools,
countries (analogue TIMSS video study)

16. Conclusions and discussion
• Proof of concept to capture classroom interaction
 technology useful
• SNA methods
• Longitudinal and temporal data can be modelled
• Challenges and limitations
– Quality of data (protocols)
– Capturing (all) interactions (and whole class?)
– Nature of the interactions
– Logistics and ethical with regard to video
– Complex character of analysis methods
– Interpretation

17. Future work
Use more advanced models
Mainly in R
• Temporal ERGM
• Rsiena
• R packages relevent (Butts, 2015) and
observR (Marcum & Butts, 2015)
Aggregate data (multilevel modelling)
• Multiple lessons into a teacher or class profile
• Multiple classes/teachers into a school
• Multiple schools into countries
17

18. Thank you
@cbokhove
www.bokhove.net
18

19. Acknowledgments
I would like to thank Thomas McDougal from the Lesson Study Alliance
for kindly providing some data from junior high schools in the USA
captured by their Lesson Note app to study the workings of the app. I
would also like to thank Dr. Hazel Brown for assisting in some of the
analyses. Finally, big thanks to staff and students of the participating
school. This project was funded by the Strategic Interdisciplinary
Research Development Fund of the University of Southampton. Ethics
approval was granted by the university, under number 9898.

20. Selected references
Bender-deMoll, S. 2014. ndtv: Network Dynamic Temporal Visualizations. R package version 0.5.1.
[Software]. Available from http://CRAN.R-project.org/package=ndtv
Butts, C.T. (2015). relevent: Relational Event Models. R package version 1.0-4, URL http:
//CRAN.R-project.org/package=relevent.
Creemers, B. P. M., & Kyriakides, L. (2008). The dynamics of educational effectiveness: A contribution to
policy, practice and theory in contemporary schools. London: Routledge
Freeman, L. (2004). The development of Social Network Analysis: A Study in the Sociology of Science.
Empirical Press.
Gephi Consortium. (2014). Gephi (Version 0.8.2 beta) [Software]. Available from https://gephi.github.io/
Givvin, K.B., Hiebert, J., Jacobs, J.K., Hollingsworth, H., & Gallimore, R. (2005). Are there national patterns
of teaching? Evidence from the TIMSS 1999 Video Study. Comparative Education Review, 49(3), 311-343.
Handcock, M.S., D. Hunter, C. Butts, S. Goodreau, P. Krivitsky, S. Bender-deMoll, and M. Morris. 2014.
Statnet: Software Tools for the Statistical Analysis of Network Data. The Statnet Project.
http://www.statnet.org. R package version 2014.2.0.
Hiebert, J., Gallimore, R., Garnier, H., Givvin, K. B., Hollingsworth, H., Jacobs, J., Chui, A. M., Wearne, D.,
Smith, M., Kersting, N., Manaster, A., Tseng, E., Etterbeek, W., Manaster, C., Gonzales, P., & Stigler,
J. (2003). Teaching Mathematics in Seven Countries: Results from the TIMSS 1999 Video Study, NCES
(2003-013), U.S. Department of Education. Washington, DC: National Center for Education Statistics.
Howe, C., & Abedin, M. (2013). Classroom dialogue: A systematic review across four decades of research,
Cambridge Journal of Education, 43(3), 325-356.
Marcum, C.S., & Butts, C.T. (2015). Constructing and Modifying Sequence Statistics for relevent Using
informR in R. Journal of Statistical Software, 64(5).
Moody, J., McFarland, D.A., & Bender-deMoll, S. (2005). Dynamic network visualization: Methods for
meaning with longitudinal network movies. American Journal of Sociology, 110, 1206-1241.

21. ADDITIONAL SLIDES

22. Results – analysis A
HK1 US1
From To Unweighted
Weighted
by duration Unweighted
Weighted
by duration
T E 44 909 26 422
T S 45 214 348 1447
E T 4 4 2 2
S T 45 185 341 833
S E 1 1 - -
S S 5 6 41 384

23. Lesson R2 Lesson R3 Lesson R5
Topic
Polynomials Vectors, statistics Standard form
Year
Year 12 Year 13 Year 9
Visualisation
Nodes (*)
13 12 26
Edges (**)
38 39 102
Degree
The size of the nodes indicates the total degree
Average degree
2.923 3.25 3.923
Avg clust.coeff.
0.399 0.305 0.496
Modularity
23