Uma modelagem diagnóstica multidimensional para o entendimento do perfil de alunos iniciantes em programação

• Ano de defesa: 2013
• Autor(a) principal: Cavalcante, Maria Cristina Tenório Cabral (Maria Cristina Tenório Cavalcante Escarpini)
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alagoas; Brasil; Programa de Pós-Graduação em Modelagem Computacional de Conhecimento; UFAL
• Palavras-chave em Português: Mineração de dados (Computação); Programação (Computadores) - Estudo e ensino; Data mining (computing); Programming (Computers)-Study and teaching; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://www.repositorio.ufal.br/handle/riufal/1775

Studies reveal learning difficulties faced by students in taking introductory programming course. This problem has been reported in various institutions, according to the literature on the subject. In this sense, the present study aims to clarify possible cognitive factors related to poor performance shown, particularly, by many students of Computer Science from the Federal University of Alagoas, concerning to the activity of solving programming problems on a beginner level. In this perspective, we have tried to know, in some cognitive respect, the mentioned students of introductory programming, considering them in different academic contexts. Particularly, we invested primarily in the analysis of groups aiming at understanding the cognitive states of these students. Furthermore, we studied two main kinds of relatioships: (i) the relationship between the performance data presented by the students in the discipline of programming and other related disciplines, as well as, (ii) and in the discipline of programming and the score expressed in the exame to access from high school to the university. To this end, we evaluated different data sources, bringing quantitative and qualitative information about the student, such as: (i) scores of all students that took this course between 2006 and 2013, (ii) scores related to disciplines, including discipline programming between 2010 and 2013, and (iii) corresponding to the set of source code produced by a sample of students on home works and reviews data. From these data, it was possible to develop a model for diagnostic profile of beginners in programming, which identified the existence of three groups of students who have cognitive characteristics ranging from students without success for those who have obtained success in the introductory programming course.