JOURNAL ARTICLE

The Impact of Educational Robotics, Virtual, and Unplugged Coding on EFL Learners' Problem-Solving, Computational Thinking, and Coding Skills.

  • Published In: Journal of Educational Computing Research, 2025, v. 63, n. 7/8. P. 1689 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Liang, Changming; Du, Lei 3 of 3

Abstract

This study investigates the comparative effects of educational robotics, virtual coding (e.g., Scratch), and unplugged coding on problem-solving skills, computational thinking (CT), and coding proficiency among English as a Foreign Language (EFL) learners. Using a pretest-posttest experimental design with 351 EFL students in China, the research found that educational robotics produced the most significant improvements across all measured domains, supported by qualitative data highlighting its tactile-visual engagement and sustained learner motivation. Virtual coding and unplugged coding also yielded gains but showed limitations related to motivational decline and abstraction challenges, respectively, with unplugged coding being particularly suitable for low-resource settings. The study emphasizes the importance of integrating multimodal, linguistically scaffolded approaches tailored to learners' proficiency and contextual resources, while calling for further longitudinal and cross-cultural research to enhance scalability and address challenges in transitioning to text-based programming languages.

Additional Information

  • Source:Journal of Educational Computing Research. 2025/12, Vol. 63, Issue 7/8, p1689
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:07356331
  • DOI:10.1177/07356331251363863
  • Accession Number:188232134
  • Copyright Statement:Copyright of Journal of Educational Computing Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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