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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Developing pre-service teachers' computational thinking: a systematic literature review.

Wei Dong1, Yongjie Li1, Lihui Sun2

  • 1School of Education, Tianjin University, Jinnan District, No. 135 Yaguan Road, Tianjin, 300354 China.

International Journal of Technology and Design Education
|February 23, 2023
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Summary

Computational thinking (CT) training significantly enhances pre-service teachers' CT abilities. This systematic review identified effective training methods and factors influencing CT, offering a model for improved teacher education.

Keywords:
Computational thinkingK-12Pre-service teacherTeacher trainingTraining methods

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Area of Science:

  • Educational Technology
  • Computer Science Education
  • Teacher Training

Background:

  • Computational thinking (CT) is increasingly vital in global education.
  • Pre-service teacher training in CT is a critical area of focus.
  • Understanding effective CT training methods is essential for educators.

Purpose of the Study:

  • To systematically analyze the literature on pre-service teachers' CT training from 2011-2021.
  • To identify effective CT training methods and influencing factors.
  • To propose a model for enhancing pre-service teachers' CT abilities.

Main Methods:

  • Systematic literature analysis of 38 studies.
  • Databases searched: Web of Science, Science Direct, and Google Scholar.
  • Analysis focused on CT training methods, effectiveness, and influencing factors.

Main Results:

  • Six distinct CT training methods were identified.
  • CT training demonstrated a positive impact on pre-service teachers' CT skills.
  • A significant relationship exists between CT ability and five key influencing factors.

Conclusions:

  • Effective CT training modules can be designed based on identified methods and factors.
  • The study provides a reference for optimizing CT training for pre-service teachers.
  • A general training model and future research trends are presented for stakeholders.