NDLI: Toward a Complete E-learning System Framework for Semantic Analysis, Concept Clustering and Learning Path Optimization

Content Provider	IEEE Xplore Digital Library
Author	Tam, V. Lam, E.Y. Fung, S.T.
Copyright Year	2012
Abstract	Most online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation.
Starting Page	592
Ending Page	596
File Size	168237
Page Count	5
File Format	PDF
ISBN	9781467316422
DOI	10.1109/ICALT.2012.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proposals Electronic learning Biological cells Materials Ontologies Optimization Clustering algorithms learning path optimization concept clustering learning objects learning styles
Content Type	Text
Resource Type	Article

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