Title
Development of a Model for Identification of Learning Standards in Distance Education using Data Mining and Meaningful Learning
Abstract
Educational data mining can be used to understand data from educational systems to provide subsidies to assist teachers, tutors and decision makers. In this context, the objective of this work was to develop a model to identify patterns of learning in distance education using Data Mining techniques and features extracted from the Meaningful Learning Theory. Seven experiments were carried out to validate the proposed model, which consisted of collecting and analyzing data about students in the seven periods of the Pedagogy course. As a result, it was possible to explain the behavior of groups of students and to validate the proposed model as an essential resource in assisting the decision-making of teachers, tutors, and managers.
Year
DOI
Venue
2019
10.1109/ICALT.2019.00065
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)
Keywords
Field
DocType
Educational Data Mining,Distance Learning,Learning Management Systems,Meaningful Learning
Subsidy,Data mining,Learning standards,Computer science,Knowledge management,Distance education,Meaningful learning,Educational systems,Educational data mining
Conference
Volume
ISSN
ISBN
2161-377X
2161-3761
978-1-7281-3486-4
Citations 
PageRank 
References 
0
0.34
0
Authors
4