Research Identification and Analysis of Clustering Algorithms for the Discovery of Engagement Profiles
DOI:
https://doi.org/10.5753/rbie.2022.2508Keywords:
Engagement, Distance Education, Interaction, Performance, Educational Data MiningAbstract
The adoption of Distance Education (EAD) is a trend that has been gaining ground in the educational field. The number of people who choose this type of education has been growing. Advantages such as flexible hours, diversity of geographic access and the use of technologies as a means of access provide an increase in adherence. Despite these benefits that are offered through the E-learning modality (Electronic Learning) and study tools such as LMS (Learning Management System), institutions still face high dropout rates and a low number of graduates. Research shows a strong link between student engagement and academic performance, which requires education managers and researchers to pay more attention to the factors that influence student engagement levels throughout the course, rather than just considering the completion rate. In this sense, this work aimed to understand the relationship between levels of engagement with academic performance. The research was divided into two phases, in the first one it sought to present a systematic review to find studies that address the phenomenon of engagement and its consequences. In the second phase, it applied educational data mining (EDM) techniques to extract and analyze behavioral data from six thousand five hundred and twenty seven students throughout an undergraduate course. As a result of the systematic review, it was possible to obtain the answers to the five research questions in the twenty-six articles returned in the IEEExplore, Science Direct and Springer search repositories. In addition, the results of the application of the EDM technique made it possible to identify three different engagement profiles, which can contribute to pedagogical decision-making, as well as the development of methodological designs that reduce dropout levels in a course.
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Copyright (c) 2022 Pamella Letícia Silva de Oliveira, Rodrigo Lins Rodrigues, Jorge Luis Cavalcanti Ramos, João Carlos Sedraz Silva
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