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The Impact of Change in the Support Networks of Trainee Secondary School Teachers of Mathematics and Science

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The Impact of Change in the Support Networks of Trainee Secondary School Teachers of Mathematics and Science

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The Impact of Change in the Support Networks of Trainee Secondary School Teachers of Mathematics and Science

  1. 1. The Impact of Change in the Support Networks of Trainee Secondary School Teachers of Mathematics and Science Christopher Downey Christian Bokhove Approaches to Longitudinal Ego Network Analysis 35th Sunbelt Conference of the INSNA – Brighton, 23rd -28th June 2015
  2. 2. Context • Teacher training in UK • PGCE – Provider Led (PL) – School Direct (SD) – NQT • Secondary Maths and Science – cohort size (~35) – Uni context – longevity of course
  3. 3. What we know already Liou, Forbes, Hsiao, Moolenaar & Daly (2013) •Pre-service elementary school teachers - mathematics – Trust and self efficacy are positively associated with pre‐ ‐ service teacher’s outcome performance on a mathematics teaching assessment. – The social network position of a pre service teacher is‐ also related to performance. •Importance of support relationships as a buffer/resilience in a pressured environment Liou, Y. , Forbes, C. A., Hsuao, J. , Moolenaar, N. and Daly, A. J. , (2013) "Investing in Potential: Exploring Preservice Teachers’ Social Capital and Outcomes" Paper presented at the annual meeting of the UCEA Annual Convention, Hyatt Regency, Indianapolis, IN Online <PDF>. 2014-12-10 from http://citation.allacademic.com/meta/p674423_index.html
  4. 4. Support networks • Instrumental – course materials – behaviour management – subject knowledge – teaching craft – academic elements • Affective – emotional support – friendship
  5. 5. Data • General – Basic demographic (sex, age) – Programme of Study (subject, mode) • Related factors – Peer trust – Self perception of development as teachers – Views on support – Network intentionality • Peer-network (bounded whole networks for Ma & Sci) • Wider network (external actors from different categories)
  6. 6. Approach • Longitudinal - 4 ‘waves’ of data collection (every 2 months) – PL and SD differences in programme structure • Directed network question: “During the last month, to whom have you turned for support?” • Both instrumental and affective aspects of support • Online questionnaire instrument – shared instruments (San Diego & Barcelona) • Multilevel modelling (MLwiN) of latent growth in dependent variables (outcomes data)
  7. 7. Data collection   Network Related factors Peer (whole) External (ego) Trust Network intent Support views Self-efficacy 1     2      3       4      
  8. 8. Research question • Are certain network characteristics significantly associated with the growth in self-efficacy of these pre-service teachers? Other outcome data is coming! • Dependent variable – teacher development (self-efficacy) – 9 point Likert scale – classroom management, student motivation, instructional skills
  9. 9. Research question • Are certain network characteristics significantly associated with the growth in self-efficacy of these pre-service teachers? Independent variables – general variables (sex, age, programme, subject) – ego network metrics (network size and homophily) – network intentionality and views on support Hope is that programme tutors might provide suggestions with regard to the importance of support networks and their composition  network intervention(?)
  10. 10. Ego network metrics
  11. 11. Assumptions • Alters other than trainees do not know each other. Every Alter outside the 75 trainees was given a unique indicator. • Alters other than trainees are nodes with out-degree zero, and can have attributes, for example ‘mentor’ or ‘family’. • Some independent variables treated as static traits. • Fellow trainees: I, everything outside: E
  12. 12. Response Rates Subject 1 2 3 4 Maths (37) 35 28 29 29 95% 81% 94% 90% Science (40) 38 33 32 31 95% 83% 86% 83% Total 73 61 61 60
  13. 13. Network intentionality
  14. 14. Support
  15. 15. Self-efficacy (development)
  16. 16. Self-efficacy (development) by programme
  17. 17. Self-efficacy (development) by subject
  18. 18. Self-efficacy (development) by gender
  19. 19. Maths (Wave 3 Example 25)
  20. 20. Maths (wave 4 >1 degree – peer network)
  21. 21. Maths (wave 4 - example 13 – High I)
  22. 22. Maths (Wave 4 - example 14 – High E)
  23. 23. Network size and homophily
  24. 24. Network homophily From homophily to heterophily
  25. 25. Network homophily – by programme
  26. 26. Multilevel analysis • Used MLwiN 2.28 • Two levels: measurements waves nested within trainees • Variance partitioned within-trainees and between-trainees • Self-efficacy (development) = β0 + β1(wave*) + β2(general) + β3(network metrics) + β4(network intent & support) + ε • wave  growth over time – random at level 2 (random slopes model)
  27. 27. Conclusions • Are certain network characteristics significantly associated with the growth in self-efficacy of these pre-service teachers? • NO!
  28. 28. Future • Tutors can take into account when students develop low number external ties • Program outcome measure: see if the development in network predicts this
  29. 29. Discussion • Independent variables change as well (e.g. network metrics). Advice on how to model this in a MLM framework (or other). • Network metrics do not seem very strong predictors of development, ‘traits’ do, however? • Curriculum mentors: are they part of the same group with peers (programme group) or external? • Moving to various forms of support rather than total support • How to resist the urge toward over-specified models

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