The Impact of Change in the Support Networks of Trainee

SecondarySchool Teachers of

Mathematics and ScienceChristopher DowneyChristian Bokhove

Approaches to Longitudinal Ego Network Analysis35th Sunbelt Conference of the INSNA – Brighton, 23rd-28th June 2015

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

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

Support networks

• Instrumental

– course materials

– behaviour management

– subject knowledge

– teaching craft

– academic elements

• Affective

– emotional support

– friendship

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)

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)

Data collection

  Network Related factorsPeer

(whole)External(ego)

Trust Network intent

Support views

Self-efficacy

1

2

3

4

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

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(?)

Ego network metrics

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

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

Network intentionality

Support

Self-efficacy (development)

Self-efficacy (development) by programme

Self-efficacy (development) by subject

Self-efficacy (development) by gender

Maths (Wave 3 Example 25)

Maths (wave 4 >1 degree – peer network)

Maths (wave 4 - example 13 – High I)

Maths (Wave 4 - example 14 – High E)

Network size and homophily

Network homophily

From homophily to heterophily

Network homophily – by programme

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)

Conclusions

• Are certain network characteristics significantly associated with the growth in self-efficacy of these pre-service teachers?

• NO!

Future

• Tutors can take into account when students develop low number external ties

• Program outcome measure: see if the development in network predicts this

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