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Exploring classroom interaction with dynamic social network analysis

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

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Exploring classroom interaction with dynamic social network analysis

  1. 1. Exploring classroom interaction with dynamic social network analysis Dr. Christian Bokhove University of Southampton SUNBELT XXXV 26th June 2015
  2. 2. Rationale • Dynamic model (Creemers & Kyriakides, 2008) – Multilevel: students in classrooms in schools – Classroom interaction • Social networks – Actors and interactions – Multidisciplinary (Freeman, 2004)
  3. 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. 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. 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. 6. Observation apps
  7. 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. 8. Data preparation 8
  9. 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. 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. 11. Analysis (US1 only) - ndtv
  12. 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. 13
  14. 14. Metrics over time 14
  15. 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. 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. 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. 18. Thank you @cbokhove www.bokhove.net 18
  19. 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. 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. 21. ADDITIONAL SLIDES
  22. 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. 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

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