A folksonomy-based recommender system for learning material prediction
Benedikt Engelbert, Karsten Morisse, University of Applied Sciences Osnabrück, Germany ; Oliver Vornberger, University of Osnabrück, Germany
EdMedia + Innovate Learning, in Victoria, Canada ISBN 978-1-939797-03-2 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
The Internet is a network where data and services can be accessed rapidly. Also in the area of eLearning it is common to access learning material online to speed up the distribution and keep the retrieve of documents easy. The variety of material increases, since teachers provide scripts/slides, but also further materials like lecture recordings or podcasts. To choose from a wide-ranging pool of material seems to be an advantage, but can also lead to disorganization, mental overload and misunderstanding of content. Many Internet services provide assistive systems so called Recommender Systems (RS), which help users to find the most important or interesting information and to overcome the mental overload. Those services may also be useful in the area of eLearning to counteract those reasons given above. In this paper we present the development of such a RS on the basis of a folksonomy approach to predict learning material in higher education and to optimize learning processes.
Engelbert, B., Morisse, K. & Vornberger, O. (2013). A folksonomy-based recommender system for learning material prediction. In J. Herrington, A. Couros & V. Irvine (Eds.), Proceedings of EdMedia 2013--World Conference on Educational Media and Technology (pp. 1590-1595). Victoria, Canada: Association for the Advancement of Computing in Education (AACE).
© 2013 Association for the Advancement of Computing in Education (AACE)
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