A Complex System Approach to Decode Different Learning Patterns in Programming between Majors: Score, Engagement, and Problem-Solving Efficiency
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| Title: | A Complex System Approach to Decode Different Learning Patterns in Programming between Majors: Score, Engagement, and Problem-Solving Efficiency |
|---|---|
| Language: | English |
| Authors: | Zhizezhang Gao, Haochen Yan, Ying Huang, Xiao Zhang, Mohammed Saqr, Xia Sun, Jun Feng (ORCID |
| Source: | Smart Learning Environments. 2026 13. |
| 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: | 21 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Systems Approach, Learning Processes, Programming, Majors (Students), Scores, Learner Engagement, Problem Solving, Efficiency, Competence, Introductory Courses, Computer Science Education, Mathematics Education, Interdisciplinary Approach |
| DOI: | 10.1186/s40561-026-00431-7 |
| ISSN: | 2196-7091 |
| Abstract: | Programming is gradually essential for non-majors but poses unique challenges compared with computer science (CS) peers. Prior cross-sectional and sequence-frequency studies overlook learning's multidimensional, emergent nature. Guided by Competency Learning Framework, we collected three-channel data: score, engagement, and problem-solving efficiency (code metrics) to jointly map student competency, including 22,950 submissions from a mixed program of 75 novices (40 CS, 35 Math) with declared majors and similar initial levels in an introductory programming course. Via complex system approach based on multi-channel longitudinal analysis, we identified three stable learning patterns (disengaged-underperformance, fluctuating, persistently engaged), along with their state-transition networks and nonlinear interactions. Each learning pattern remains relatively stable throughout the semester, consistent with the general dynamics of a complex system. Hardworking students in the fluctuating are similar, whereas the disengaged-underperformance and persistently engaged differ across majors, indicating that declared major influences attractor states of student groups. CS students emerged as early strivers with stronger learning consistency, whereas Math prefer late engagers with considerable proportion of learning avoidance and cold-start. This highlights the importance of initial states: those starting behind faced greater inertia. We contribute detail methodology for process-oriented programming research via complex system approach which reveals who learns, how, and when. Furthermore, our findings uncover how theoretical frameworks manifest in learning patterns and bridge gap between abstract theory and observable programming learning process, which are readily extendable to other educational contexts of higher education beyond CS. Based on these insights, we offer process-oriented guidance and scaffolding for students and teachers. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1503515 |
| Database: | ERIC |
| Abstract: | Programming is gradually essential for non-majors but poses unique challenges compared with computer science (CS) peers. Prior cross-sectional and sequence-frequency studies overlook learning's multidimensional, emergent nature. Guided by Competency Learning Framework, we collected three-channel data: score, engagement, and problem-solving efficiency (code metrics) to jointly map student competency, including 22,950 submissions from a mixed program of 75 novices (40 CS, 35 Math) with declared majors and similar initial levels in an introductory programming course. Via complex system approach based on multi-channel longitudinal analysis, we identified three stable learning patterns (disengaged-underperformance, fluctuating, persistently engaged), along with their state-transition networks and nonlinear interactions. Each learning pattern remains relatively stable throughout the semester, consistent with the general dynamics of a complex system. Hardworking students in the fluctuating are similar, whereas the disengaged-underperformance and persistently engaged differ across majors, indicating that declared major influences attractor states of student groups. CS students emerged as early strivers with stronger learning consistency, whereas Math prefer late engagers with considerable proportion of learning avoidance and cold-start. This highlights the importance of initial states: those starting behind faced greater inertia. We contribute detail methodology for process-oriented programming research via complex system approach which reveals who learns, how, and when. Furthermore, our findings uncover how theoretical frameworks manifest in learning patterns and bridge gap between abstract theory and observable programming learning process, which are readily extendable to other educational contexts of higher education beyond CS. Based on these insights, we offer process-oriented guidance and scaffolding for students and teachers. |
|---|---|
| ISSN: | 2196-7091 |
| DOI: | 10.1186/s40561-026-00431-7 |
