Internet Technology Levels in a Higher Education Teaching and Learning Environment: Discriminant Function Analysis Approach

Internet Technology Levels in a Higher Education Teaching and Learning Environment: Discriminant Function Analysis Approach

John K. Rugutt (Illinois State University, USA), Lucille L. T. Eckrich (Illinois State University, USA) and Caroline C. Chemosit (Lincoln College, USA)
DOI: 10.4018/978-1-4666-2988-2.ch009


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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There has been increasing interest in the use of internet technology to augment traditional teaching techniques, and this increasing interest is not surprising and may be considered inevitable as the delivery of instruction lends itself to a system of mass higher education (Maye, 1998). For instance, Web-enhanced learning is certainly an option that offers instructors a range of advantages, such as providing feedback with relative ease and, thus, providing a more flexible pace of learning while reaching a wider and more diverse audience (Collis, De-Boer, & Slotman, 2001; Hoskins & Van Hooff, 2005; Hoskins, Newstead, & Dennis, 1997). Plous (2000) and Ward and Newlands (1998) note that with access to computer and internet resources, wider markets of learners are harnessed.

With computer resources, learners are provided with the opportunity and reason to interface with computers on a regular basis. Further, library resources are largely retrievable online and students do not have to be in a physical location like a library facility to be able to access most research articles and technical research reports. With advanced computer technology and library online databases, doing research has been made much easier for learners. Further, with computer resources, learners can improve their computer literacy, which is a critical component for the employment market of the future (Heinssen, Glass, & Knight, 1987; Miura, 1987).

Literature is replete with studies that highlight the benefits of web-based learning. Most of these studies indicate that the use of educational technology affords students greater anonymity and opportunities to practice a range of generic skills (for instance, management of self, others, task, and information) (Howe, 1998; Oliver & McLoughlin, 2001). Further, through online technologies, learners can profit from an interactive and engaging environment with a range of learning scaffolds and supports thus enabling them to broaden and make sense of their experience (Hammond & Trapp, 2001; Krantz & Eagly, 1996).

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