Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence.

ABSTRACT: This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students' performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students'

SUBMITTER: Subirats L 

PROVIDER: S-EPMC9970089 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature