ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire
E-Learning recommender system effectiveness re- lies upon their ability to recommend appropriate learning con- tents according to the learner learning style and preferences. 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 learning style and preferences of the learner on a domain before adapting the learning process and course content. This study focuses on identifying the learning styles of students in order to adapt the learning process and course content. ULEARN is an adaptive recommender learning system designed to provide learners with personalised learning environment such as course learning objects that match their adaptive profile. This paper presents the algorithm used in ULEARN to reduce dynamically the number of questions in Felder-Silverman learning style ques- tionnaire used to initialise 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.
The file attached to this record is the author's final peer reviewed version.
Citation : Nafea, S., Siewe, F. and He, Y. (2018) ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire.IEEE Intelligent Systems Conference 2018, London, September 2018.
Research Group : Software Technology Research Laboratory (STRL)
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes