Adaptive Help System Based on Learners ‘Digital Traces’ and Learning Styles
Keywords:TEL, data mining, Learning Styles, traces interactions, user profile.
Learning management system (LMS) such as Claroline, Ganesha, Chamilo, Moodle ..., are commonly and well used in e-education (e-learning). Most of theTechnology Enhanced Learning (TEL) focus on supporting teachers in the creation and organization of online courses. However, in general, they do not consider individual differences of each learner. In addition, they do not provide enough indicators which will help to track the learners. In this paper, we investigate the benefits of integrating learning styles in the Web-based educational systems. Also we are interested in the use of interaction traces in order to address the lack of feedback between the learner and the teacher. Generally, we aim to offer a tool that allows the tutor and the instructional designer to interpret learner courses, in order to provide help as needed for each individual.
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