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Student Hits in an Internet‐Supported Course: How Can Instructors Use Them and What Do They Mean?
Author(s) -
Baugher Varanelli Weisbord Dan, Andrew, Ellen
Publication year - 2003
Publication title -
decision sciences journal of innovative education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 19
eISSN - 1540-4609
pISSN - 1540-4595
DOI - 10.1111/j.1540-4609.2003.00016.x
Subject(s) - consistency (knowledge bases) , regression analysis , class (philosophy) , stepwise regression , computer science , the internet , variables , statistics , mathematics education , psychology , world wide web , mathematics , artificial intelligence
The world of education is changing as Web‐based technology and courseware are increasingly used for delivery of course material. In this environment, instructors may need new measures for determining student involvement, and ultimately student performance. This study examines whether hits to a Web site have any value for predicting student performance in a traditional course supported by Web activities. Total Hits at the end of the semester was used as one measure. Hit Consistency, determined by assigning a 0 when no hits occurred between class meetings and by assigning a 1 when one or more hits occurred between class meetings, was another. Hit Consistency was significantly correlated with course average ( r = .37, p < .001) for 108 students in two course sections. Hit Consistency started to show a significant relationship with course average by the third week (or class). Total Hits was not found to significantly correlate with course average ( r = .08, p > .05) at the end of the semester or during any week. These results suggest that students who consistently access a Web site will perform better than those who do not. When Hit Consistency and Total Hits were entered as independent variables into a stepwise regression with course average as the dependent variable, the model was enhanced by the addition of Total Hits after Hit Consistency was entered ( R = .43, p < .001) . Hierarchical regression analysis in which cumulative grade point average was entered as the first controlling variable suggested that online access may go beyond the predictive value of achievement alone for predicting course performance with Hit Consistency appearing to be the dominant causal variable.