2026-09-20T19:42:58Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/109392025-10-09T00:05:32Zcom_10259_5645com_10259_5086com_10259_2604com_10259_6193com_10259_4147com_10259.4_106col_10259_10943col_10259_7108
Repositorio Institucional de la Universidad de Burgos
author
Porras Alfonso, Santiago
author
Sauvée, Athénaïs
author
Puche Regaliza, Julio César
author
Casado Yusta, Silvia
author
Antón Maraña, Paula
author
Pacheco Bonrostro, Joaquín
2025-10-08T08:14:40Z
2025-10-08T08:14:40Z
2024-06
978-84-13962-00-9
https://hdl.handle.net/10259/10939
10.4995/HEAd24.2024.17343
Absenteeism in higher education is a problem that may involve institutional, economic,
social, and individual consequences. The present work aims to analyse whether the way
students manage their personal time could be an explanation for absenteeism rates.
Authors used machine learning based methodology, combined with explainable artificial
intelligence methods. This allowed them to design a two-levels analysis, it is to say from
a global, and an individual perspective. Factors such as repeating a course have the
most negative impact over class attendance. On the contrary, being able to submit an
assignment before the deadline has the most positive impact over class attendance. The
kind of academic career, the place of living or the hobbies has also influence over the
absenteeism.
eng
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Absenteeism
Higher education
Support vector machine
Explainable artificial intelligence
Shapley additive explanation
Time management
Time management and absenteeism: studying the students through machine learning
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/10939/1/Porras_HEAd2024_676-680.pdf
File
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application/pdf
Porras_HEAd2024_676-680.pdf