Predição de evasão de estudantes non-majors em disciplina de introdução à programação

Resumo

Student dropout is commonly observed in introductory programming courses at undergraduate courses in exact sciences and engineering (CS1 for non-majors). Through the use of data mining techniques on student information from a Brazilian higher education institution, it was observed that socioeconomic elements, unrelated to the teaching-learning environment, can act both as catalysts and as neutralizers of this dropout process. Based on this inference and the use of supervised machine learning algorithms, this work supports the elaboration of a predictive model for early identification of dropout non-majors students at CS1