the impact of change in the support networks of trainee secondary school teachers of mathematics and...
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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
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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
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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
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Support networks
• Instrumental
– course materials
– behaviour management
– subject knowledge
– teaching craft
– academic elements
• Affective
– emotional support
– friendship
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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)
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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)
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Data collection
Network Related factorsPeer
(whole)External(ego)
Trust Network intent
Support views
Self-efficacy
1
2
3
4
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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
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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(?)
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Ego network metrics
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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
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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
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Network intentionality
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Support
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Self-efficacy (development)
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Self-efficacy (development) by programme
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Self-efficacy (development) by subject
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Self-efficacy (development) by gender
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Maths (Wave 3 Example 25)
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Maths (wave 4 >1 degree – peer network)
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Maths (wave 4 - example 13 – High I)
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Maths (Wave 4 - example 14 – High E)
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Network size and homophily
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Network homophily
From homophily to heterophily
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Network homophily – by programme
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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)
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Conclusions
• Are certain network characteristics significantly associated with the growth in self-efficacy of these pre-service teachers?
• NO!
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Future
• Tutors can take into account when students develop low number external ties
• Program outcome measure: see if the development in network predicts this
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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