Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines.

Mobile personalized learning can be achieved by the identification of students' learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires' choices, and thus, erroneous adaptation to students' needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires' choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students' learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students' learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.