At present, computer programming skills are essential in engineering curricula and professional practice. In spite of this, and after decades of research in programming pedagogy, academic success in introductory programming courses continues to be a challenge for many students. In this research we explore the feasibility of predicting academic results in a modular computer programming course in a Chilean university (N=242), through measurement of psychometric variables linked to implicit theories of intelligence, error orientation, and students attitudes towards programming. Coincidentally with other recent studies conducted in Finland and Turkey, early measurement of implicit theories of intelligence did not emerge as a predictor of academic performance in the programming course. As for error orientation, students exhibiting mild measures of an error strain construct did seem to perform better than students with extreme measures. The variables with the highest predictive potential were found to be students’ attitudes towards programming; namely, their perceived value of programming skills, and perception of programming self-efficacy. Substantial differences were noted in both latter constructs among male and female students. We discuss implications of our findings and future research prospects.
|Título de la publicación alojada||LALA 2019 - Proceedings of the 2nd Latin American Conference on Learning Analytics|
|Editores||Eliana Scheihing, Julio Guerra, Valeria Henriquez, Cristian Olivares, Pedro J. Munoz-Merino|
|Número de páginas||10|
|ISBN (versión digital)||9788416829385|
|Estado||Publicada - 2019|
|Evento||2nd Latin American Conference on Learning Analytics, LALA 2019 - Valdivia, Chile|
Duración: 18 mar. 2019 → 19 mar. 2019
Serie de la publicación
|Nombre||CEUR Workshop Proceedings|
|ISSN (versión impresa)||1613-0073|
|Conferencia||2nd Latin American Conference on Learning Analytics, LALA 2019|
|Período||18/03/19 → 19/03/19|
Nota bibliográficaFunding Information:
★ Research funded by CONICYT Fondecyt Initiation into Research grant 11160211.
© 2019 CEUR-WS. All rights reserved.
- Computer programming course
- Engineering education
- Predictive analytics
- Psychometric variables