Identification of learning styles in technological platforms (LMS) through decision trees
DOI:
https://doi.org/10.55777/rea.v12i23.1213Keywords:
Educational data mining, Knowledge management systems, Learning stylesAbstract
In the present study, patterns and usage characteristics indicate how a student adapts to educational resources in a technological platform (LMS, for its acronym Learning Management System) based on their learning style, according to the theory of Felder and Silverman (1988), using the data mining of decision trees
technique are identified. The study involved 130 Software Engineering students at a university in the south of Sonora, and the knowledge discovery methodology in databases (KDD) was used. It was found that visual, sensitive and balanced styles can be predicted correctly in 75% of cases. The evidence suggests that the styles proposed in the selected theory are not fulfilled one hundred percent according to their initial conceptualization. This may be due to the fact that the theory of learning styles was not designed to identify learning styles in students who are taking courses in distance modality in terms of the dimensions proposed by their authors. In a breakdown of the materials available to the students, it was evident that all learning styles show a preference to those of text type; even the visual students.
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