Un modelo predictivo del rendimiento académico a partir de las calificaciones de Bachillerato y PAU

  1. Roberto Dorta Guerra 1
  2. Isabel Marrero 1
  3. Beatriz Abdul-Jalbar 1
  4. Rodrigo Trujillo González 1
  5. Néstor Torres Darias 1
  1. 1 Universidad de La Laguna

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

De los procesos de cambio al cambio con sentido
  1. Vega Navarro, Ana (coord.)

Publisher: Servicio de Publicaciones ; Universidad de La Laguna

ISBN: 978-84-16471-20-1

Year of publication: 2019

Pages: 119-136

Type: Book chapter

DOI: 10.25145/B.INNOVAULL.2019.009 DIALNET GOOGLE SCHOLAR lock_openRIULL editor


Several educational investigations have shown that the academic performance in the first year of university affects the success in subsequent years, which justifies the interest of analyzing the performance, during the first year, of new students in order to identify the factors that influence it. In the present work, a new indicator of this performance has been defined and those indicators of previous performance which are best correlated with the indicator introduced have been determined for each one of the degrees in Science of the ULL. We have thus obtained multivariate linear regression models that allow us to predict the performance of new students in the first semester of the first year of each degree, based on their performance in High School and the University Access Test. In all of Science degrees, the dominant predictor variable has turned out to be the High School grade point average. The goodness of fit of the models that use the new indicator far exceeds that of other pre-existing models in the literature. Our method is extensible to any degree and university. Its systematic application would allow defining and detecting academic risk profiles so that, on the one hand, affected students may be encouraged to adopt a proactive attitude towards the correction of their training deficiencies and, on the other hand, university managers can optimize the human and material resources necessary to improve the academic performance of those students at risk.