Multi-criteria based negotiation for learning content selection : submitted in partial fulfilment of the requirements for the degree of Master of Information Sciences, Massey University, New Zealand
Loading...
Date
2006
DOI
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Massey University
Rights
The Author
Abstract
With the rapid evolution of information technology and continuous expansion of all sorts of content on the internet, enormous opportunities have become available for learners to enhance their learning. Consequently, learners need effective support mechanisms that assist them in efficiently selecting the most appropriate learning content for achieving their learning goal, rather than blindly grabbing materials that are largely available on the internet. However, it is a challenging problem to provide appropriate learning content selection facilities for the learners to efficiently identify learning content that best suit their needs, due to the large varieties of the factors that influence the process of learning content selection. Previous research has presented various solutions targeting the facilitation of learning content selection. Many of them provide content selection rules by simply grouping learners into different pedagogical categories merely based on limited theories or designers' own judgments Disadvantages of these approaches are obvious: the lack of comprehensive supports of pedagogical theories reduces the preciseness and reliability of the content selection results. Based on the literature review regarding the factors that influence learning content selection, standardized educational metadata, and current computer software technologies, this project therefore proposes a web based interactive system for learning content selection by introducing a multi-criteria decision making methodology. Based on the methodology, a mechanism for matching learning content with subject matter characteristics of the learning resources and learner's preference is developed. By taking dynamic and interrelated parameters as user inputs, recommendations for the content selection are generated based on the built-in parameter dependency rules.
Description
Keywords
Computer-assisted instruction, Educational technology, Internet in higher education