Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment
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Authors
International Journal on E-Learning, 2005 in Norfolk, VA ISSN 1537-2456
Abstract
In this paper, we proposed an evolving e-learning system which can adapt itself both to the learners and the open the web and pointed out the differences of making recommendations in e-learning and other domains. We propose two pedagogy features in recommendation: learner interest and background knowledge. A description of paper value, similarity, and ordering are presented using formal definitions. We also study two pedagogy-oriented recommendation techniques: content-based and hybrid recommendations. We argue that while it is feasible to apply both of these techniques in our domain, a hybrid collaborative filtering technique is more efficient to make "just-in-time" recommendations. In order to assess and compare these two techniques, we carried out an experiment using artificial learners. Experiment results are encouraging, showing that hybrid collaborative filtering, which can lower the computational costs, will not compromise the overall performance of the RS. In addition, as more and more learners participate in the learning process, both learner and paper models can better be enhanced and updated, which is especially desirable for web-based learning systems. We have tested the recommendation mechanisms with real learners, and the results are very encouraging
Citation
Tang, T. & McCalla, G. (2005). Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning, 4(1), 105-129. Norfolk, VA: Association for the Advancement of Computing in Education (AACE). Retrieved August 10, 2024 from https://www.learntechlib.org/p/5822.
© 2005 AACE