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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

By Halil Ibrahim Cebeci, Harun Resit Yazgan and Abdulkadir Geyik

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

Topics: T Technology (General), LB Theory and practice of education, LC Special aspects of education
Publisher: Taylor and Francis Ltd
Year: 2009
DOI identifier: 10.1080/09687760802649889
OAI identifier: oai:generic.eprints.org:851/core5

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