Treffer: The Development and Validation of the Questionnaire to Characterize Learning Strategies in Computer Programming (CEAPC)

Title:
The Development and Validation of the Questionnaire to Characterize Learning Strategies in Computer Programming (CEAPC)
Language:
English
Source:
Journal of Educational Computing Research. 2024 61(8):103-138.
Availability:
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed:
Y
Page Count:
36
Publication Date:
2024
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Geographic Terms:
DOI:
10.1177/07356331231183450
ISSN:
0735-6331
1541-4140
Entry Date:
2023
Accession Number:
EJ1402088
Database:
ERIC

Weitere Informationen

This article presents the design, construct validation, and reliability of a self-report instrument in Spanish that aims to characterize different types of strategies that students can use to learn computer programming. We provide a comprehensive overview of the identification of learning strategies in the existing literature, the design and development of preliminary questionnaire items, the refinement of item wording, and the examination of the internal structure and reliability of the final instrument. The construction of the items was based on the educational theory of Self-Regulated Learning. The final version of the questionnaire, called the Computer Programming Learning Strategies Questionnaire (CEAPC), was administered to 647 students enrolled in computer programming courses. The data collected from the participants were used to examine the construct validity and reliability of the questionnaire. The CEAPC consists of 13 subscales, each corresponding to a different type of learning strategy, and a total of 89 items. Statistical analyses of the data indicate that the CEAPC has adequate construct validity. In addition, the results of the internal consistency analysis indicate satisfactory reliability across the different subscales of the instrument. This study contributes to the field of educational research, particularly in the area of self-regulated learning in computer programming.

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