Many educators believe that the most effective means for teaching is through one-on-one interactions with students. This research's hypothesis is that better learning results would be achieved by adapting the e-tutor interaction with its individual student user. eTutor-Student interaction in this research is based on adapting the content and presentation of the learning material to the student based on his/her learning model-The student model. In other words, eTutor should adapt and personalize the teaching strategy for each student; something that is not easy to achieve without the aid of an intelligent system with a comprehensive knowledgebase. This article presents one essential component of our research on adaptive e-learning-namely, a framework of a Smart Cognitive Augmented Learning Object Repository (SCALOR) engine that augments the concepts of learning styles onto Hypermedia Learning Objects, which together with a Smart domain knowledge ontology compose the Smart e-Learning Knowledgebase (SELK). SELK is at the core of the personalization of the eTutor-Student interaction for a more efficient and effective learning process. Evaluation results for this framework proved the hypothesis.
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