A Comparative Analysis of the Effects of Instructional Design Factors on Student Success in E-Learning: Multiple-Regression versus Neural Networks

Autor/inn/en	Cebeci, Halil Ibrahim; Yazgan, Harun Resit; Geyik, Abdulkadir
Titel	A Comparative Analysis of the Effects of Instructional Design Factors on Student Success in E-Learning: Multiple-Regression versus Neural Networks
Quelle	In: ALT-J: Research in Learning Technology, 17 (2009) 1, S.21-31 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0968-7769
DOI	10.1080/09687760802649889
Schlagwörter	Correlation; College Students; Grade Point Average; Academic Achievement; Foreign Countries; Comparative Analysis; Instructional Effectiveness; Design Requirements; Courses; Instructional Design; Computer Software; Computer Assisted Instruction; Electronic Learning; Educational Technology; Turkey + Suchen Sie Ihr Suchwort? Korrelation; Collegestudent; Schulleistung; Ausland; Unterrichtserfolg; Design preferences; Gestaltungsmittel; Kursangebot; Lesson concept; Lessonplan; Unterrichtsentwurf; Computer based training; Computerunterstützter Unterricht; Unterrichtsmedien; Türkei
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. (Contains 6 tables and 2 figures.) (As Provided).
Anmerkungen	Co-Action Publishing. Ripvagen 7, SE-175 64 Jarfalla, Sweden. Tel: +46-18-4951138; e-mail: info@co-action.net; Web site: http://www.co-action.net/journals/Journals_index.php?
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10