Predição de desempenho no Moodle usando princípios da andragogia

• Ano de defesa: 2020
• Autor(a) principal: Trindade, Fernando Ribeiro
• Orientador(a): Ambrósio, Ana Paula Laboissière
• Banca de defesa: Rodrigues, Cássio, Siqueira, Sean Wolfgand Matsui, Ferreira, Deller James
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/38995/001300000bvdn
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Predição desempenho; Tutoria; Moodle; EaD; Framework
• Palavras-chave em Inglês: Performance prediction; Mentoring; Moodle; Distance learning; Framework
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/10632

According to current literature, the teaching skills of tutors are essential to ensure excellence in teaching and, consequently, the interest of students in courses. In online teaching environments, students and tutors interact with each other through the various communication resources provided by virtual learning environments (VLE). With this, a large amount of educational data is collected by AVAS’s, making it possible to carry out analyzes of these data. However, in the academic literature, few studies have been conducted in order to collect behavioral data from tutors and use this data to make the prediction of students' school performance. Therefore, in this dissertation a framework of tutoring characteristics was elaborated correlated to the good school performance of students, and this framework was used to guide the data collection of tutors, which were used to make the prediction of student performance. The tutoring characteristics included in the framework were extracted from previous research, which investigated each tutoring attribute, and from tutoring attributes desired by Andragogy. The prediction of students' performance was carried out from the development of an extension of the Moodle Predicta tool, which performs classification of students as to possible failure or approval. The prediction of student performance is made from the behavioral data of students and tutors. The implementation of the prediction was preceded by a performance analysis of the classifying algorithms, and the implemented classifier was RandomForest, which achieved better performance according to the AUC metric. Educational data from Moodle from the Goiás Judicial School (EJUG) was used in a case study. Two exploratory data analyzes were conducted to learn about the courses and investigate the tutoring characteristics of the framework in EJUG tutors. The data from EJUG tutors were included in the classification model, used to predict student performance, showing that the actions of tutors can impact students' academic achievements.