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Cognitive Enhancement through Competency-Based Programming Education: A 12-Year Longitudinal Study

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Title: Cognitive Enhancement through Competency-Based Programming Education: A 12-Year Longitudinal Study
Language: English
Authors: Dunhong Yao (ORCID 0000-0002-5742-9583), Jing Lin (ORCID 0009-0004-3235-1718)
Source: Education and Information Technologies. 2025 30(14):20347-20383.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 37
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Competency Based Education, Computer Science Education, Programming, Longitudinal Studies, Undergraduate Students, Foreign Countries, Cognitive Ability, Intelligence Tests, Computation, Thinking Skills, Problem Solving, Cognitive Development, Learner Engagement, Predictor Variables, Student Projects, Student Participation, Innovation, STEM Education
Geographic Terms: China
Assessment and Survey Identifiers: Raven Progressive Matrices
DOI: 10.1007/s10639-025-13582-w
ISSN: 1360-2357
1573-7608
Abstract: Programming education consistently faces challenges in bridging theory with practice and fostering students' cognitive competencies. This 12-year longitudinal study (2011-2023) investigates an innovative competency-based teaching model in university C programming education that integrates six educational theories into a coherent framework with three dimensions (theoretical, practical, innovative), four integration mechanisms, and five combinatorial strategies. Using a mixed-methods approach with a quasi-experimental design, we studied 4,051 undergraduate students from a Chinese university. Results revealed significant enhancement in students' cognitive abilities, as measured by Raven's Standard Progressive Matrices (t (350) = 8.76, p < 0.001, d = 0.68), which strongly correlated with improved academic performance (r = 0.62), computational thinking (r = 0.71), and problem-solving skills (r = 0.67). The model creates multiple pathways for cognitive development through synergistic interactions between components, promoting collaboration and self-directed learning with effects extending beyond graduation. Multiple regression analysis identified three key predictors of cognitive enhancement: classroom engagement ([beta] = 0.35), project completion ([beta] = 0.28), and participation in innovation activities ([beta] = 0.22). This study provides robust empirical evidence for the long-term efficacy of a competency-based model in programming education, presenting a transformative approach to STEM education reform particularly relevant in rapidly evolving technological landscapes.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484060
Database: ERIC
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  Data: Programming education consistently faces challenges in bridging theory with practice and fostering students&#39; cognitive competencies. This 12-year longitudinal study (2011-2023) investigates an innovative competency-based teaching model in university C programming education that integrates six educational theories into a coherent framework with three dimensions (theoretical, practical, innovative), four integration mechanisms, and five combinatorial strategies. Using a mixed-methods approach with a quasi-experimental design, we studied 4,051 undergraduate students from a Chinese university. Results revealed significant enhancement in students&#39; cognitive abilities, as measured by Raven&#39;s Standard Progressive Matrices (t (350) = 8.76, p &lt; 0.001, d = 0.68), which strongly correlated with improved academic performance (r = 0.62), computational thinking (r = 0.71), and problem-solving skills (r = 0.67). The model creates multiple pathways for cognitive development through synergistic interactions between components, promoting collaboration and self-directed learning with effects extending beyond graduation. Multiple regression analysis identified three key predictors of cognitive enhancement: classroom engagement ([beta] = 0.35), project completion ([beta] = 0.28), and participation in innovation activities ([beta] = 0.22). This study provides robust empirical evidence for the long-term efficacy of a competency-based model in programming education, presenting a transformative approach to STEM education reform particularly relevant in rapidly evolving technological landscapes.
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      – SubjectFull: Competency Based Education
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