Um modelo computacional para classificação da motivação de estudantes em educação on-line

• Ano de defesa: 2016
• Autor(a) principal: Malta, Cheops Araujo
• 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: Programação de computadores – Ensino; Ferramenta de auxílio a aprendizagem; Motivação na educação; Mineração de dados educacionais; Motivation to Learn; Computer programming – Teaching; Motivation in education; Educational data mining; Distance education; Achievement goals; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://www.repositorio.ufal.br/handle/riufal/2452

In 2014, Brazil offered 25,166 courses through distance education. Data show that the distance education (Educação a Distância - EAD, in portuguese) is not a fad, but rather part of a broad and continuous process of change, and attest the consolidation phase of this type of education, especially in higher education, showing significant and sustainable growth. However, there are still many pedagogical and technological challenges. One of these challenges is related to the motivation to learn. Studies show that the reasons and the goals that lead students whether to engage learning, or not, is important from a motivational point of view, as well as a key issue to help educators understanding the processes of learning and their most important constructs. In this sense, we propose a model to automatically classify the motivation to learn from students of online/distance education. The model is generated with the aid of Psychometric instruments and Educational Data Mining. It is developed in three stages that occur sequentially, starting from the "construction of the database," where students’ data are collected through psychometric instruments (questionnaires) and logs (recorded actions and interactions in the virtual learning environment). In the next step, we carried out an experiment to "algorithm selection for classification" to be used in the construction of models. Finally, at the stage of "model building" is constructed and validated the model for student motivation classification.