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A comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks

Cebeci, Halil Ibrahim and Yazgan, Harun Resit and Geyik, Abdulkadir (2009) A comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks. Association for Learning Technology Journal, 17 (1). pp. 21-31. ISSN 0968-7769 (print)/1741-1629 (online)

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    Abstract

    This study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple regression and supervised artificial neural network (ANN) models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success.

    Item Type: Article
    Uncontrolled Keywords: e-learning; distance education; instructional design factors; multimedia systems; artificial neural networks
    Subjects: T Technology > T Technology (General)
    L Education > LB Theory and practice of education
    L Education > LC Special aspects of education
    Divisions: ALT-J Journal
    Depositing User: Users 126 not found.
    Date Deposited: 25 Nov 2010 11:59
    Last Modified: 04 Apr 2011 09:56
    URI: http://repository.alt.ac.uk/id/eprint/851

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