Learning Styles Analysis based on Pattern Recognition Techniques

This paper presents an analysis of different learning styles observed in a group of college freshmen. Recognizing relevant aspects of each style provides aid in the planning of actions that could reduce dropouts and increase the academic performance of first-year students in colleges. To accomplish this study an assessment tool was devised and implemented applying techniques of clustering and measurement of the attributess informational weight. The results showed distinctive attributes that allow them to be classified within five groups (learning styles) that were labeled according to the Felder-Silverman model, and from said groups the visual as well as the active style appear dominant in the case study.