ULEARN: Personalised Learner’s Profile Based On Dynamic Learning Style Questionnaire

The file attached to this record is the author's final peer reviewed version.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

A Gaze-Based Intelligent Textbook Manager

IT technologies and their rapid development can greatly support and significantly improve Human-Computer Interaction, defining new communication methods for a fast and direct user experience. One very promising technology nowadays is eye tracking. The main contribution of our research is to propose a gaze-based intelligent textbook manager that will support complete document analysis. Our solution includes tools providing translation support (single or entire sentences), keywords annotations, and the creation of document summaries, that contain all the areas that the user has read, with their time and words of major interest