Alejandro Rabasa (Miguel Hernández University of Elche,), Kristina Polotskaya (Miguel Hernández University of Elche), Agustín Pérez-Martín (Miguel Hernández University of Elche), Nuria Mollá (Teralco Solutions Ltd. and Miguel Hernandez University of Elche) and Patricia Compañ (University of Alicante)
Abstract:
Early detection of demotivation patterns is a key tool that could allow the design of educational strategies to reduce it. These patterns may vary depending on the socio-economic factors of the students, their careers and the subjects of study in each case. Therefore, the large amount of input information justifies the use of Machine Learning techniques to generate predictive models to manage this issue. This article presents the case study of Data Science subjects in careers of two different universities. The computational experiments are carried out surveying 168 students from a total of 21 courses, belonging to 13 different university degree programmes, assigned to different faculties or schools of two universities. The paper presents some of the most relevant demotivation patterns and a battery of proposals to reduce it.