Influence of Motivation and Learning Strategies on Performance in Introductory Programming

Publication Date

10-21-2025

Document Type

Conference Proceeding

Publication Title

Comped 2025 Proceedings of the ACM Global Computing Education Conference 2025

Volume

1

DOI

10.1145/3736181.3747151

First Page

301

Last Page

308

Abstract

Programming is a core computer science competency, and mastering its theoretical foundations and practical skills is essential for success in any computing discipline. Despite its importance, we have observed a high failure rate in the introductory programming courses at our university. Therefore, it is important to investigate the factors that influence performance in introductory programming at a university level. This study examines the relationship between factors related to motivation and learning strategies and performance in an introductory programming course within the African context. Data were collected from 138 students enrolled in an introductory programming course. The instrument used in this study is the Motivated Strategies for Learning Questionnaire (MSLQ), which was designed to assess college students' motivations and learning strategies. The study's findings reveal a statistically significant positive correlation between students' self-efficacy, intrinsic goal orientation, effort regulation, and task value, and their performance in programming. On the other hand, test anxiety showed a significant but negative correlation with performance in introductory programming. Furthermore, a stepwise multiple linear regression analysis revealed that self-efficacy, test anxiety, and extrinsic goal orientation collectively account for 24.6% of the variation in programming performance. The results suggest that instructors and students need to work on improving students' motivation and learning strategies to improve performance in introductory programming.

Keywords

factors influencing performance, introductory programming, motivation and learning strategies, programming performance

Department

Computer Science

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