Um modelo computacional de apoio à análise da opinião de alunos sobre práticas docentes por meio da mineração de dados educacionais
Tipo
Tese
Data de publicação
2017-04-27
Periódico
Citações (Scopus)
Autores
Santos, Fábio de Paula
Orientador
Silveira, Ismar Frango
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Silva, Leandro Augusto da
Omar, Nizam
Araújo Junior, Carlos Fernando de
Yamamoto, Cláudio Haruo
Omar, Nizam
Araújo Junior, Carlos Fernando de
Yamamoto, Cláudio Haruo
Programa
Engenharia Elétrica
Resumo
The Institutional Teacher’s Evaluation besides being a legal need in higher education is an important moment for any Educational Institution. Traditionally, questionnaires with closed answer questions are used for this purpose and many times the evaluation is left to a secondary place. This work proposes a computational model based in machine learning techniques and Sentiment Analysis that allows increasing the scope of this evaluation when allowing the use of open and textual questions. The application of these techniques in Educational Data Mining context provides basis to decision-making based on the students’ opinions. For this purpose, as proof of concept, a mining of a student’s opinion survey from a Vocational High School in Brazil was held and categorized their sentiments as positive or negative in relation to their lecturers’ techniques with supervised machine learning approach. This model also contemplates clustering analysis to find categories of analysis of student opinions through an unsupervised Learning Machine model. As a conclusion it was proven that the use of tools for textual analysis of open questions is possible and it to speeds up the decision-making of institutional evaluations.
Descrição
Palavras-chave
mineração de dados educacionais , aprendizado de máquina , análise de sentimentos , avaliação institucional docente
Assuntos Scopus
Citação
SANTOS, Fábio de Paula. Um modelo computacional de apoio à análise da opinião de alunos sobre práticas docentes por meio da mineração de dados educacionais. 2017. 115 f. Tese (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2017.