INDeLER: Elearning personalization by mapping student's learning style and preference to metadata

Abstract

This paper presents the process of developing Student profile by mapping students categories explored with Felder- Soloman's ILS questionnaire to the appropriate value of the personalization vector XYZ, and by deriving vector's values from the acquired student's answers on Preference test. Obtained vector values perform the PeLCoM metadata and they provide recommendations for creating personalized eLearning experience. The architecture of Personalized eLearning System INDeLER is presented. INDeLER system derives student's profile, provides sequencing of personalized eLearning sessions and supports scenario for designing lessons content tailored to the individual student needs. Further, we describe how the personalization system INDeLER includes teacher's influence to the eLearning experience by composing different pedagogical aspects and corresponding didactic and methodological processes to the unique way of teaching tailored to the particular student needs. The example of INDeLER personalization process is also shown.