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. Keywords: e-learning; distance education; instructional design factors; multimedia systems; artificial neural networks DOI: 10.1080/09687760802649889
Highlights
For the past few decades, we have witnessed the widespread application of technological developments in the educational systems
The aim of this study is to establish a relationship between instructional design factors and the student success level
This study investigated the effects of instructional design factors on students’ success using multiple-regression and artificial neural network (ANN) approaches as prediction models
Summary
For the past few decades, we have witnessed the widespread application of technological developments in the educational systems. The integration of education technologies with communication technologies introduced e-learning as an alternative or complementary approach for the traditional education methods that are subject to time and location constraints. The necessity of having easier access to a wider range of information, coupled with the demand for effective and low cost education, paved the way for the increased use of the Internet in learning environments. One of the challenging problems that society has been facing is the need to improve the quality of e-learning systems. A number of studies have been conducted to gain a better understanding of the factors that affect the success of e-learning systems and the interrelations of these factors. The literature can be categorised into three main groups, namely, student and instructor characteristics (Bozarth, Chapman, and LaMonica 2004; Greene et al 2004; Hillsheim 1998; Kerr, Rynearson, and Kerr 2006; Liaw, Huang, and Chen 2007; Marom et al 2003; Soong et al 2001), instructional design factors (Bozarth, Chapman, and LaMonica 2004; Wegner, Holloway, and Garton 1999), and demographic variables (Diaz 1999; Diaz 2000; Dille and Mezack 1991; Hillsheim 1998; Lynn 2002; Marom et al 2003; Muse 2003)
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