Analysis of Student Performance and Social-economic Data in Introductory Computer Science Courses at the University of Brasília

• Main Authors: Fonseca Silveira, Rodrigo, Holanda, Maristela, Ramos, Guilherme N., Victorino, Marcio, Da Silva, Dilma
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Computer languages; CS1; Data models; Education; Educational Data Mining; Introduction to Computer Science course; Machine Learning; Mechatronics; Trajectory
• ISSN: 2377-634X
• DOI: 10.1109/FIE56618.2022.9962408

Computer Science 1 (CS1) courses introduce undergraduate students to computational thinking and their first programming language. As in most institutions, CS1 is a challenge for students at the University of Brasilia, one of the top 10 universities in Brazil. In 2012, the Brazilian higher education system changed with an affirmative-action policy to admit more students from the public K-12 system: the "Quota" Law was implemented at all federal public universities. This paper aims to answer two research questions: 1) What knowledge about the positive/negative impact of certain features on the success of a CS1 course can be discovered from mining educational data augmented by social-economic information? 2) Are these features different between quota and non-quota students? The analysis uses social-economic and academic performance data of undergraduate students from 2012 to 2019. Data mining algorithms such as generalized linear model, gradient boosting machine, and random forest were applied to the data. The findings include: (1) the relevance of indicators such as the consumption rate of university-subsidized meals, (2) that gender is not a determining factor in failure/success, and (3) a higher failure rate for quota students in the Computer Engineering and Mechatronics Engineering majors.