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Self‐Regulated Learning Strategies in Computer Programming Education.

  • Published In: European Journal of Education, 2025, v. 60, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ramírez‐Echeverry, Jhon Jairo; Restrepo‐Calle, Felipe; Jiménez, Stephanie Torres 3 of 3

Abstract

This study investigates the self‐regulated learning strategies employed by students in computer programming courses. Utilising the Questionnaire on Learning Strategies in Computer Programming (CEAPC), the research aims to identify specific strategies used by students. The findings reveal a variety of effective learning strategies, including problem‐solving, knowledge acquisition and study environment management in the context of computer programming learning. However, difficulties in idea organisation were noted, suggesting a need for enhanced support in structuring and documenting thought processes and code. The study also highlights the interdependence of learning strategies, particularly the role of metacognition in conjunction with practice, problem‐solving and time management. Differences in strategy use across course levels and gender were observed, with advanced courses prompting more complex strategies and female students excelling in structured and collaborative learning. These insights provide educators with valuable guidance for developing targeted interventions to improve students' self‐regulated learning abilities in programming education. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:European Journal of Education. 2025/03, Vol. 60, Issue 1, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0141-8211
  • DOI:10.1111/ejed.70052
  • Accession Number:183654467
  • Copyright Statement:Copyright of European Journal of Education is the property of Wiley-Blackwell 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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