Predição de desempenho no Moodle usando princípios da andragogia
Nenhuma Miniatura disponível
Data
2020-05-15
Autores
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
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.
Descrição
Palavras-chave
Predição desempenho , Tutoria , Moodle , EaD , Framework , Performance prediction , Mentoring , Moodle , Distance learning , Framework
Citação
TRINDADE, F. R. Predição de desempenho no Moodle usando princípios da andragogia. 2020. 147 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.