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Use of educational data mining to identify distance learning students' profiles and patterns of participation
Thaiza Ramos (Universidade Federal do Rio Grande do Norte), Apuena Vieira Gomes (Universidade Federal do Rio Grande do Norte), Márcia Jacyntha Nunes Rodrigues Lucena (Universidade Federal do Rio Grande do Norte), Isabel Dillmann Nunes (Universidade Federal do Rio Grande do Norte), Ricardo Alexsandro de Medeiros Valentim (Universidade Federal do Rio Grande do Norte), Giovani Ângelo S. da Nóbrega (Universidade Federal do Rio Grande do Norte)
Abstract: The Educational Data Mining allows to identify and cluster students by certain characteristics that are specified through the needs and problems raised by the teachers and course coordinators. In this context, this research aims to apply data mining techniques for the identification of profiles and participation patterns of students in a course of higher distance course resulting in the prediction of the chances of each student's approval. The result was the identification of 4 clusters that designate the profiles and types of participation of the students, namely: active, medium, inconstant and absent.