Análise do contexto e do resultados da aprendizagem da avaliação educacional em um curso de graduação em Engenharia

• Ano de defesa: 2015
• Autor(a) principal: Vasconcelos, Francisco Herbert Lima
• Orientador(a): Mota, João César Moura
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Teleinformática; Engenharia - Avaliação educacional; Aprendizagem - Avaliação; Matriz decomposition; Tensor decomposition; Keating outcomes
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/52636

Educational evaluation provides methods to obtain data that can be useful for eva- luating groups of individuals (students, teachers, administrators, technicians and others), projects, products and materials, educational institutions and systems at different levels and skills. In engineering education, evaluation processes can help managers to make decisions and changes in undergraduate courses. This thesis investigates in unprecedented way a new approach to the analysis and interpreta- tion of data in the field of engineering education with emphasis in the evaluation process, taking into account two aspects in an integrated manner: a) perception / opinion of students about the context / educational environment (Learning Con- text - LC) and b) the results / income earned by the same students (Learning outcomes - LO). For this research, we collected data related to undergraduate students in Teleinformatics Engineering (TEI), at Technology Center (CT) of the Federal University of Cearà (UFC). LC data were collected from the application of SEEQ (Student’s Evaluation of Educational Quality) instrument of SETE (Stu- dent Teaching Evaluate Effetivecness) methodology. The LO data was collected from the information of the performance of the students’ learning outcomes. Car- rying out the information processing of the obtained tensor and matrix data, we have used two mathematical tools: the bilinear decomposition, called Principal Composent Analysis - PCA decomposition and the multilinear tensor decompœ sition by Parallel Factor Analysis - PARAFAC. The results allow us to identify common features and similarities in curriculum components, both in terms of per- ception as the performance of students. The PCA and PARAFAC models also showed significant potential to extract data information related to latent variables in educational settings