By Topic

Multi-faceted Learning Paths Recommendation Via Semantic Linked Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Juan Yang ; Dept. of Comput. Sci., Sichuan Normal Univ., Chengdu, China ; ZhiXing Huang ; Hongtao Liu

Cognition overload is one of the major problems in current self-learning intelligent learning systems. Providing learners with the personalized learning path can effectively smooth over users' learning disorientation. In this paper, we propose a multi-faceted recommendation framework that provides learners with personalized learning paths based on their different learning styles. Building the recommendation system mainly involves the following three steps: (1) analyze the influences of the learning style in different dimensions during the learning process, (2) automatically organize the Learning Objects (LOs) into a multi-faceted Semantic Linked Network (SLN) via self-organized rules, (3) recommend the learning path to the learner through a reasoning machine based on the constructed SLN. The experiments verify the efficiency of the proposed method.

Published in:

Semantics Knowledge and Grid (SKG), 2010 Sixth International Conference on

Date of Conference:

1-3 Nov. 2010