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Automatic E-learning contents composition by using gap analysis techniques

Published: 06 July 2009 Publication History

Abstract

A goal of e-learning is to increase efficiency by precisely identifying the training a student needs, and providing that training in the context of day to day activities of the students [1]. This paper describes how the use of IA algorithms can be utilized to automatically generate customized learning contents on elearning environments. On top of that, we also propose the inclusion in the whole process of digital rights, as in our opinion, it's a way to make up for contents creators and to encourage them to create better, more useful materials.

References

[1]
De Coi, J. L., Herder, E., Koesling, A., Lofi, C., Olmedilla, D., Papapetrou, O., Siberski, W. A Model for Competence Gap Analysis. Proceedings of 3rd International Conference on Web Information Systems and Technologies (WEBIST), 2007.
[2]
IEEE, Learning Technology Standards Committee (LTSC). Draft Standard for Learning Technology -- Data Model for Reusable Competency Definitions. 2007, IEEE.
[3]
IMS, Reusable Definition of Competency or Educational Objective -- Information Model. 2002, IMS Global Learning Consortium

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    cover image ACM Conferences
    ITiCSE '09: Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
    July 2009
    428 pages
    ISBN:9781605583815
    DOI:10.1145/1562877
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 06 July 2009

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    Author Tags

    1. E-learning
    2. LMS
    3. learning object
    4. sequencing

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    ITiCSE '09 Paper Acceptance Rate 66 of 205 submissions, 32%;
    Overall Acceptance Rate 552 of 1,613 submissions, 34%

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