Utilising the student model in distance learning
ITiCSE '98 Proceedings of the 6th annual conference on the teaching of computing and the 3rd annual conference on Integrating technology into computer science education: Changing the delivery of computer science education
Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoring and Delivering Adaptive Web-Based Textbooks Using WEAR
ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Authoring of learning styles in adaptive hypermedia: problems and solutions
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Reappraising cognitive styles in adaptive web applications
Proceedings of the 15th international conference on World Wide Web
The evolution of metadata from standards to semantics in E-learning applications
Proceedings of the seventeenth conference on Hypertext and hypermedia
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
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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.