Thursday, June 27
3:25-3:45 PM
PDT
Esquimalt

A folksonomy-based recommender system for learning material prediction

Brief Paper: New Development ID: 39886
  1. aaa
    Benedikt Engelbert
    University of Applied Sciences Osnabrück
  2. aaa
    Karsten Morisse
    University of Applied Sciences Osnabrück
  3. Oliver Vornberger
    University of Osnabrück

Abstract: 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.

Presider: Lyudmila Smirnova, Mount Saint Mary College

Topics

Conference attendees are able to comment on papers, view the full text and slides, and attend live presentations. If you are an attendee, please login to get full access.
x