University of Tasmania
Browse

File(s) under permanent embargo

Using the STEM framework collegially for mentoring, peer learning and planning

journal contribution
posted on 2023-05-19, 18:14 authored by Susan KilpatrickSusan Kilpatrick, Sharon FraserSharon Fraser
Effective professional learning communities are crucial for supporting and developing the practice and identity formation of beginning teachers. Professional networks facilitate collegial learning and continuous improvement of professional practice of all teachers, and are especially important for out of field teachers. Rural practice is characterised by professional isolation, the need to be a ‘specialist generalist’ and broad work and social networks that rarely include others working in a closely related professional specialisation. Science, Technology, Engineering and Mathematics (STEM) in rural schools is frequently taught by beginning or out of field teachers. Many rural schools therefore lack depth and breadth in their school-based professional learning network to meet STEM teachers’ needs. This paper reports findings from a research project, based upon a pragmatic qualitative design, in which teachers developed a framework to assist beginning and out of field, rural STEM teachers identify appropriate resources for their context. A qualitative evaluation of its trial suggests professional learning networks among schools can enhance the framework’s effectiveness.

Funding

Department of Industry, Science, Energy and Resources

History

Publication title

Professional Development in Education

Volume

45

Issue

4

Pagination

614-626

ISSN

1941-5257

Department/School

Faculty of Education

Publisher

Routledge

Place of publication

United Kingdom

Rights statement

Copyright 2018 International Professional Development Association (IPDA)

Repository Status

  • Restricted

Socio-economic Objectives

Teacher and instructor development

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC