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Development of a Mobile Application for Learning Style Prediction

Development of a Mobile Application for Learning Style Prediction
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Author(s): Eugenia Olaguez Torres (Universidad Politécnica de Sinaloa, Mexico), Piero Espino (Universidad Politécnica de Sinaloa, Mexico) and Jonathan Garcia (Universidad Politécnica de Sinaloa, Mexico)
Copyright: 2019
Pages: 20
Source title: Educational Technology and the New World of Persistent Learning
Source Author(s)/Editor(s): Liston W. Bailey (University of Phoenix, USA)
DOI: 10.4018/978-1-5225-6361-7.ch010


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This chapter presents the development of a mobile application through the use of intelligent systems adapted to learning styles in accordance to the models of Feder-Silverman and Kolb. This development takes place in a Java programming language within the Android Studio development environment, which at the same time uses the SQLite mobile data base. The mobile application allowed the authors to identify the learning styles of students from the Mechatronics Engineering academic program that show some sort of educational backwardness in the subject of differential calculus. It was found that, according to the Felder-Silverman model, the style that predominates among students is the auditory style, while in accordance to the Kolb model, it was identified that the reflective style was the most common learning style amongst mechatronics students. It is concluded that through the use of this mobile application, students are able to identify the learning style, predict, and apply appropriate learning techniques to their learning style.

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