Prevenire l’abbandono universitario

• Autores: Gabriella Giulia Pulcini, Valeria Polzonetti, Loredana Cappellacci, Lorenzo Cesaretti
• Localización: Transformando la educación a través del conocimiento / coord. por José María Esteve Faubel, Aitana Fernández Sogorb, Rosabel Martínez Roig, Juan Francisco Álvarez Herrero, 2022, ISBN 978-84-19506-73-3, págs. 1046-1060
• Idioma: italiano
• Enlaces
  • Texto Completo Libro  1    2  
• Resumen

  • Academic students’ skills and learning styles are key factors that can affect the academic perma-nence and/or contribute to dropping out. This paper presents a preliminary study conducted to investigate about the relationship between specific learning styles and socio-cultural factors by machine learning techniques.The analyses were performed on data acquired by the blended learning course “Know yourself. Discover your learning style”. The course aims to support students to investigate about their own abilities and soft skills and to improve them by specific train activities. The course has proposed to students enrolled on the 1st and 2ndyear of all bachelor degree courses of the University of Camerino starting from 2016. From the collected data, 195 data set have been selected for realize by machine learning techniques to develop, train, and optimise mathematical models with the objective to identify the student’s profile with the higher possibility of dropout.Identifying students with greatest risk of dropping out could be of great interest for the tutoring service. In particular, a series of actions could be planned to enhance the use of methodologies and tools suitable for improving skills useful to complete the university course. The obtained results from data processing are promising.