Ulearn: Personalised learner’s profile based on dynamic learning style questionnaire

Abstract

E-Learning recommender system effectiveness relies upon their ability to recommend appropriate learning contents that match the learner preferences and learning style. An effective approach to handle the learner preferences is to build an efficient learner profile in order to gain adaptation and individualisation of the learning environment. It is usually necessary to know the preferences and learning style of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learners’ learning styles in order to adapt the course content and learning process. ULEARN is an adaptive recommender learning system devised to provide personalised learning environment, such as course learning objects that match the learner’s adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style questionnaire used to initialize the adaptive learner profile. Firstly, the questionnaire is restructured into four groups, one for each learning style dimension; and a study is carried out to determine the order in which questions will be asked in each dimension. Then an algorithm is built upon this ranking of questions to calculate dynamically the initial learning style of the user as they go through the questionnaire.