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Internet Technology Levels in a Higher Education Teaching and Learning Environment: Discriminant Function Analysis Approach
Abstract
The authors of this study utilized the discriminant function analysis using extreme student groups (top and bottom quartiles) defined by students’ internet technology scores to develop a model that best predicts group membership of the low and high internet technology levels among college students. The sample for the study was drawn from a Midwestern doctoral university and consisted of a random sample of senior year undergraduate students (n = 537). The instrument for the study used items from a 2000 College Student Survey (HERI, 2000). The response format for most instrument subscale items used in this study was of the Likert-type. Results of the discriminant analysis showed that students’ classification into low or high internet technology groups based on the institutional, behavioral, and personality variables can accurately be done. The lowest total percent correctly classified was at 72% while the highest total percent correctly classified was 74%. The variables that made significant differences included: student faculty interaction, student support services, quality of instruction and college experience, interpersonal relations and leadership, and student extra effort in learning.
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