Predicting academic results in a modular computer programming course

Claudio Alvarez, Alyssa Wise, Sebastian Altermatt, Ignacio Aranguiz

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)


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.
Idioma originalInglés
Título de la publicación alojadaLALA 2019 - Proceedings of the 2nd Latin American Conference on Learning Analytics
EditoresEliana Scheihing, Julio Guerra, Valeria Henriquez, Cristian Olivares, Pedro J. Munoz-Merino
Número de páginas10
ISBN (versión digital)9788416829385
EstadoPublicada - 2019
Evento2nd Latin American Conference on Learning Analytics, LALA 2019 - Valdivia, Chile
Duración: 18 mar. 201919 mar. 2019

Serie de la publicación

NombreCEUR Workshop Proceedings
ISSN (versión impresa)1613-0073


Conferencia2nd Latin American Conference on Learning Analytics, LALA 2019

Nota bibliográfica

Funding Information:
★ Research funded by CONICYT Fondecyt Initiation into Research grant 11160211.

Publisher Copyright:
© 2019 CEUR-WS. All rights reserved.

Palabras clave

  • Computer programming course
  • Engineering education
  • Predictive analytics
  • Psychometric variables


Profundice en los temas de investigación de 'Predicting academic results in a modular computer programming course'. En conjunto forman una huella única.

Citar esto