Curriculum content for teaching teachers' best-beloved self competencies: A Meta-Synthesis Study

Document Type : Original Article

Authors

1 Management and Educational planning, Psychology and Educational Sciences Faculty, Shiraz University, Shiraz city

2 faculty of shiraz university

3 faculty member of Shiraz university

4 Primary and preschool education .Faculty of Shiraz

5 Associate proffessor in curriculum studies faculty of education & psychology of shiraz university

Abstract

The purpose of the current research was to present the content framework of the curriculum for teaching the competencies of the best-loved self for the training of teachers. The content of the curriculum of best-loved self-included various skill, attitudinal and knowledge dimensions. The current research is a synthesis research design that was carried out with a qualitative approach, using a meta-synthesis method based on the model of Sandelowski and Barroso.44 research articles were retrieved from the database and 33 of these articles were used for research purposes. Based on meta- synthesis analysis, 93 codes,18 concepts and 3 dimensions were identified. The curriculum content of the teachers' best-loved self in three dimensions , attitudinal dimension (autonomy, mental awareness or present life, self-affirmation and ...), skill-practical (imagination Experiences, personal imagery, autobiography,...)and knowledge dimension( familiarity with social links and storytelling and narrative strategies, strategies self -promotion and development, knowledge construction and reconstruction strategies...) ,were named. Finally, based on the findings of the research, a content framework for teaching the competencies of the best-loved self of teachers was extracted, to be used by planners as a roadmap for content creation in the teacher training curriculum.

Keywords

Journal of Curriculum Studies
Volume 18, Issue 70 - Serial Number 70
September 2023
Pages 177-210

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History

  • Receive Date: 31 August 2022
  • Revise Date: 25 November 2022
  • Accept Date: 03 December 2022

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