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

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. 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. 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. Support networks
• Instrumental
– course materials
– behaviour management
– subject knowledge
– teaching craft
– academic elements
• Affective
– emotional support
– friendship

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. 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. Data collection
Network Related factors
Peer
(whole)
External
(ego)
Trust Network
intent
Support
views
Self-efficacy
1    
2     
3      
4      

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. 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. Ego network metrics

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. 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. Network intentionality

14. Support

15. Self-efficacy (development)

16. Self-efficacy (development) by programme

17. Self-efficacy (development) by subject

18. Self-efficacy (development) by gender

19. Maths (Wave 3 Example 25)

20. Maths (wave 4 >1 degree – peer network)

21. Maths (wave 4 - example 13 – High I)

22. Maths (Wave 4 - example 14 – High E)

23. Network size and homophily

24. Network homophily
From homophily to heterophily

25. Network homophily – by programme

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)

28. Conclusions
• Are certain network characteristics significantly
associated with the growth in self-efficacy of these
pre-service teachers?
• NO!

29. Future
• Tutors can take into account when students develop low
number external ties
• Program outcome measure: see if the development in
network predicts this

30. 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