Predicting academic results in a modular computer programming course

Claudio Alvarez, Alyssa Wise, Sebastian Altermatt, Ignacio Aranguiz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationLALA 2019 - Proceedings of the 2nd Latin American Conference on Learning Analytics
EditorsEliana Scheihing, Julio Guerra, Valeria Henriquez, Cristian Olivares, Pedro J. Munoz-Merino
Number of pages10
ISBN (Electronic)9788416829385
StatePublished - 2019
Event2nd Latin American Conference on Learning Analytics, LALA 2019 - Valdivia, Chile
Duration: Mar 18 2019Mar 19 2019

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference2nd Latin American Conference on Learning Analytics, LALA 2019


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

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'Predicting academic results in a modular computer programming course'. Together they form a unique fingerprint.

Cite this