TY - GEN
T1 - Validation of an Emotional Model in Programming Learning in the University Context
AU - Avilés-Castillo, Fátima
AU - Quezada-Sarmiento, Pablo Alejandro
AU - Sabando-García, Ángel Ramón
AU - Buele, Jorge
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Programming learning in higher education constitutes a complex academic experience, characterized by continuous cycles of problem solving, trial and error, feedback, and performance assessment. Beyond cognitive demands, affective and motivational factors are involved, influencing students’ persistence, engagement, and self-regulation. In this context, the aim of this study was to conduct the initial validation of an emotional model for programming learning in a university population through structural equations. The model was structured into five latent factors: academic emotions, motivation (intrinsic and extrinsic), emotional self-perception, the emotional climate of the educational environment, and emotional engagement. A quantitative, non-experimental, cross-sectional design was implemented with a sample of 159 university students enrolled in programming courses in Ecuador. Data were collected using a 100-item online questionnaire in English (20 per dimension), with a five-point Likert scale. Analyses were conducted in SPSS and AMOS using maximum likelihood estimation, assessing sampling adequacy, internal consistency, model fit, and convergent and discriminant validity. The results showed high sampling adequacy (KMO = 0.898) and strong internal consistency of the instrument (overall α = 0.988). The model demonstrated satisfactory overall fit (CMIN/DF = 2.371) and significant covariances among factors, highlighting the associations between climate and emotional engagement and between motivation and emotional engagement. Convergent validity was adequate; however, discriminant analysis suggested high proximity between some constructs, which may reflect conceptual overlap at this initial stage. The model provides robust psychometric evidence for characterizing emotional dimensions linked to programming learning. Future work should refine and reduce items, evaluate model stability across other samples, and strengthen discrimination among factors to optimize its application in real-world university teaching contexts.
AB - Programming learning in higher education constitutes a complex academic experience, characterized by continuous cycles of problem solving, trial and error, feedback, and performance assessment. Beyond cognitive demands, affective and motivational factors are involved, influencing students’ persistence, engagement, and self-regulation. In this context, the aim of this study was to conduct the initial validation of an emotional model for programming learning in a university population through structural equations. The model was structured into five latent factors: academic emotions, motivation (intrinsic and extrinsic), emotional self-perception, the emotional climate of the educational environment, and emotional engagement. A quantitative, non-experimental, cross-sectional design was implemented with a sample of 159 university students enrolled in programming courses in Ecuador. Data were collected using a 100-item online questionnaire in English (20 per dimension), with a five-point Likert scale. Analyses were conducted in SPSS and AMOS using maximum likelihood estimation, assessing sampling adequacy, internal consistency, model fit, and convergent and discriminant validity. The results showed high sampling adequacy (KMO = 0.898) and strong internal consistency of the instrument (overall α = 0.988). The model demonstrated satisfactory overall fit (CMIN/DF = 2.371) and significant covariances among factors, highlighting the associations between climate and emotional engagement and between motivation and emotional engagement. Convergent validity was adequate; however, discriminant analysis suggested high proximity between some constructs, which may reflect conceptual overlap at this initial stage. The model provides robust psychometric evidence for characterizing emotional dimensions linked to programming learning. Future work should refine and reduce items, evaluate model stability across other samples, and strengthen discrimination among factors to optimize its application in real-world university teaching contexts.
KW - Academic emotions
KW - Affective computing
KW - Educational climate
KW - Emotional engagement
KW - Programming
UR - https://www.scopus.com/pages/publications/105045601959
U2 - 10.1007/978-3-032-30781-1_33
DO - 10.1007/978-3-032-30781-1_33
M3 - Contribución a la conferencia
AN - SCOPUS:105045601959
SN - 9783032307804
T3 - Lecture Notes in Computer Science
SP - 502
EP - 514
BT - Learning and Collaboration Technologies - 13th International Conference, LCT 2026, Held as Part of the 28th HCI International Conference, HCII 2026, Proceedings
A2 - Smith, Brian K.
A2 - Borge, Marcela
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Learning and Collaboration Technologies, LCT 2026, held as part of the 28th HCI International Conference, HCII 2026
Y2 - 26 July 2026 through 31 July 2026
ER -