What drives undergraduates’ effort and persistence in learning programming

This study attempts to: (a) investigate whether positive and negative emotions mediate the pathways linking self-efficacy for learning programming with effort and persistence in undergraduates’ learning Scratch programming combining with a programmable hardware platform (i.e., Arduino), and (b) asse...

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Bibliographic Details
Published in:Education and information technologies Vol. 28; no. 10; pp. 12383 - 12406
Main Authors: Lin, Guan-Yu, Liao, Yi-Wen, Su, Zhi-Yuan, Wang, Yu-Min, Wang, Yi-Shun
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2023
Springer
Springer Nature B.V
Subjects:
Academic Persistence
College students
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computers and Education
Education
Educational Technology
Emotions
Information Systems Applications (incl.Internet)
Information Technology
Least Squares Statistics
Majors (Students)
Mediation
Methods
Persistence
Positive Attitudes
Predictor Variables
Programming
Psychological Patterns
Self Efficacy
Structural Equation Models
Undergraduate Students
User Interfaces and Human Computer Interaction
Taiwan
Persistence
Learning programming
Emotions
Academic discipline
Self-efficacy
Effort
ISSN:1360-2357, 1573-7608
Online Access:Get full text
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Summary:This study attempts to: (a) investigate whether positive and negative emotions mediate the pathways linking self-efficacy for learning programming with effort and persistence in undergraduates’ learning Scratch programming combining with a programmable hardware platform (i.e., Arduino), and (b) assess the effect of academic major (i.e., information technology-related majors vs non-information technology-related majors) on self-efficacy for learning programming and emotions. With the use of responses collected from a sample of 156 undergraduate students, the research model is empirically validated using partial least squares structural equation modeling (PLS-SEM). The findings reveal that undergraduates’ effort and persistence while learning programming were significantly predicted by positive emotions, but not by negative emotions. Self-efficacy for learning programming significantly and positively influenced positive emotions and negatively influenced negative emotions. Finally, variations in predicting positive emotions were found between different academic majors: students in information technology-related majors experienced less negative emotional experiences than those in non-information technology-related majors.
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ISSN:1360-2357
1573-7608
DOI:10.1007/s10639-023-11670-3