Algorithmic Abstraction and Mathematical Knowledge on Rates of Dropout from Computing Degree Courses

Abstract

The general purpose of the present study was to analyze dropout from Brazilian Computing degree courses, based on data provided by INEP and a case study carried out at the University of Brasilia (UnB), in which a student was considered circumvented when “disengage[d] from the course for any reason different from degree obtainment. Dropout rates were analyzed in order to check a possible correlation with the number of applicants per place, the influence of gender, and a possible relationship between the requirements of algorithmic abstraction and mathematical knowledge and rates of dropout from courses in the major areas of Science, Mathematics, and Computing. Dropout was computed by tracking each student's status between 2010 and 2014 in the eight key areas classified by the Organisation for Economic Co-operation and Development (OECD), as well as the area of Computing. Data was reviewed to see if there was any evidence that factors like algorithmic abstraction, the number of applicants per spot, or the gender of students had an impact on dropout. the present study cannot reach the overall conclusions for such a complex and vast subject, it does provide some evidence for the influence of requirements regarding knowledge of mathematics and algorithmic abstraction on the dropout of students from Computing degree courses. It should be emphasized that the survey of circumvented students was performed in a public institution (UnB).