Predicting academic performance using automatic learning techniques: A review of the scientific literature

Molina-Astorayme J.; Cabanillas-Carbonell M. · Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020 (2020) · DOI: 10.1109/eircon51178.2020.9254065

Resumen

Considering the problems and challenges faced by educational institutions in analyzing student performance and improving their educational management, the various automatic learning techniques were examined, which will allow them to generate accurate predictions through the data collected from their students. The present research is a systematic review of literature based on the articles published in IEEE Xplore, Scopus, Science Direct and Scielo where 80 articles were found that according to our inclusion and exclusion criteria were systematized 47. We observed the various techniques used for automatic learning to develop predictive models based on academic performance, we can determine that the most used techniques were the classification. In this way, automatic learning techniques will allow educational institutions to publicize the academic performance of their students in order to improve the educational quality they offer.

Instituciones: Universidad Privada del Norte