Portability of Predictive Academic Performance Models: An Empirical Sensitivity Analysis

• Main Authors: Jose Luis Arroyo-Barrigüete, Susana Carabias-López, Tomas Curto-González, Adolfo Hernández
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Mathematics
• Subjects: mathematics education; university teaching; academic success; quantitative research; predictive models; portability
• Online Access: https://www.mdpi.com/2227-7390/9/8/870

The portability of predictive models of academic performance has been widely studied in the field of learning platforms, but there are few studies in which the results of previous evaluations are used as factors. The aim of this work was to analyze portability precisely in this context, where preceding performance is used as a key predictor. Through a study designed to control the main confounding factors, the results of 170 students evaluated over two academic years were analyzed, developing various predictive models for a base group (BG) of 39 students. After the four best models were selected, they were validated using different statistical techniques. Finally, these models were applied to the remaining groups, controlling the number of different factors with respect to the BG. The results show that the models’ performance varies consistently with what was expected: as they move away from the BG (fewer common characteristics), the specificity of the four models tends to decrease.