Year: 2024
Type: conference-paper
Abstract: In recent years, there has been a significant increase in the student dropout rate in Higher Education. Various reasons are cited, such as difficulty learning the content, the proposed course structure, and lack of financial resources. This study explores machine learning (ML) in the student dropout problem. The experiments were conducted with a dataset from the Academic System of a Federal University listing different academic features of the students. They showed promising results, with Random Forest accurately predicting the student situation with an average F-Score of 0.959. However, most relevant features are expected and do not provide any new insight regarding the dropout imminence. Future experiments can fix it with a more robust feature engineering process.
Authors: Pedro Mian Parra, Muriel De Souza Godoi, Francisco Carlos Monteiro Souza, Alinne C. Correa Souza, Edgar De Souza Vismara, Rafael Gomes Mantovani
Institution: Universidade Tecnológica Federal do Paraná
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4404849564