Banca de DEFESA: LUCAS MARCHISOTTI DE SOUZA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : LUCAS MARCHISOTTI DE SOUZA
DATE: 08/04/2024
TIME: 15:00
LOCAL: https://meet.google.com/eva-tdvh-wdw
TITLE:

Models for predicting student dropout in undergraduate courses at the Federal University of São João del-Rei

KEY WORDS:
PAGES: 82
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUMMARY:

Student dropout is the abandonment of studies by formally enrolled students. This phenomenon is influenced by different factors, such as loss of interest, financial difficulties, lack of investment, and appropriate policies, among others. The impacts of evasion reach different levels, from the student's interrupted training to the waste of resources and even society, which loses a professional future. Although the analysis of evasion is a complex task, the advancement of computational techniques such as artificial intelligence has enabled analysis and inference from data, aiming to understand and even predict this phenomenon. This work aims to predict dropout rates in the context of higher education at the Federal University of São João del-Rei (UFSJ), considering periods before and during the COVID-19 pandemic, through the application of machine learning techniques. Furthermore, evaluate the main variables related to the evasion phenomenon in both periods in search of differences. Data from students who graduated and dropped out between 2018 and 2021 from in-person undergraduate courses at UFSJ are included in the analysis and creation of prediction models. Sociodemographic variables, information related to the course, and student performance are selected as possible predictors. The initial data analysis made it possible to evaluate the quality of the available data and the frequency distribution of the variables and to carry out their preparation. After preparation, the database has 7.305 records and 23 variables in addition to the target class. The CRISP-DM methodology commanded understanding the business and data, preparing data, creating models, and evaluating. Based on similar works, the use of Decision Tree and Random Forest algorithms presented accuracy, precision, AUC, and F1-score rates close to or above 90%, revealing Random Forest as the holder of the best results. Analyzing the relevance of the variables in the results, attributes of academic performance and social/financial assistance were expected to be important and confirmed the expectation in both periods analyzed. Other attributes have already shown movements of gain or loss in the transition or even low significance between periods. In short, the results of this study reveal a promising performance of the model proposed in the analysis of student dropout at UFSJ. Furthermore, the insights gained about student characteristics provide a basis for developing preventive and student support strategies.

BANKING MEMBERS:
Interno - 2058929 - ELDER JOSE REIOLI CIRILO
Presidente - 1857559 - FERNANDA SUMIKA HOJO DE SOUZA
Externo à Instituição - RAFAEL ALVES BONFIM DE QUEIROZ - UFOP