Identifying Demotivation Patterns in Studentsof Subjects Related to Data Science at College

Autores: Alejandro Rabasa Dolado , Kristina Polotskaya, Agustín Pérez Martín, Nuria Mollà, Patricia Compañ Rosique
Localización: Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality / coord. por Francisco José García Peñalvo , Alicia García Holgado , 2023, ISBN 978-981-99-0941-4, págs. 690-698
Idioma: inglés
Texto completo no disponible (Saber más ...)
Resumen
- Early detection of demotivation patterns is a key tool that could allowthe design of educational strategies to reduce it. These patterns may vary dependingon the socio-economic factors of the students, their careers and the subjects of studyin each case. Therefore, the large amount of input information justifies the use ofMachine Learning techniques to generate predictive models to manage this issue.This article presents the case study of Data Science subjects in careers of twodifferent universities. The computational experiments are carried out surveying168 students from a total of 21 courses, belonging to 13 different university degreeprogrammes, assigned to different faculties or schools of two universities. Thepaper presents some of the most relevant demotivation patterns and a battery ofproposals to reduce it.