18: Statistics and Machine Learning in the Analysis of Undergraduate Programs at a Brazilian University:

Music City Center 

The decrease in demand and dropout rates in undergraduate programs in exact and technological sciences are problems that have implications for society, affecting the job market and potentially leading to a shortage of professionals. Statistical and machine learning methods can contribute to a better understanding of this phenomenon and assist in the design of actions at universities. The objective of this work is to outline the student profile and identify factors for dropout, completion, and retention in undergraduate programs. The research was conducted on a technological sciences campus, including students in engineering, computing, and mathematics teaching. Data from the academic system were used. Exploratory analysis and data visualization techniques were applied to construct the profile. Logistic regression and tree-based models were used to identify the success factors in program completion. The partial results indicate that the student profile varies according to the program. In mathematics and engineering programs, the dropout rate is high and first-phase courses have a strong impact on student dropout,indicating the need for specific actions aimed at incoming students.

Data visualization

Logistic regression

Higher education

Student profile

Student success 

Isolated Statisticians