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Article Excerpt
Students from five high schools participated in a 2 to 4-week microcomputer based laboratory (MBL) physics curriculum in two groups. One group completed the curriculum entirely online, and the other completed the same curriculum in a traditional classroom setting. Variables were collected to predict student success on a post-unit measure of physics ability through correlations. Traditional, literature-suggested, variables such as math ability, physics aptitude, and demographic information were combined with variables related to the mode of delivery of instruction. Several computer-related variables, such as students' access to a computer at home and how often students accessed the Internet were included in the models. Math ability and physics aptitude dominated the models regardless of the method of delivery of instruction. Computer-related variables were included in the model for the online group; however, they were not significant.
INTRODUCTION
The ability to predict the success or failure of a student in a class is a very powerful tool. If specific skills are highly correlated with success then every effort should be made to ensure that all students have these necessary skills. If a lack of certain skills correlates highly with failure, then students should receive remediation until their skills match those of the students deemed likely to succeed. Studies have been conducted using a wide variety of intellectual, socio-emotional, and background variables in an attempt to predict students' success or failure. These studies have been conducted in an effort to help teachers understand their students, to assist universities in selecting students to enter their institutions, and for advising students on classes necessary as prerequisites to afford these students their greatest opportunity for success. Adding to this genre of research, this study examined if there were any traditional or computer-related variables that could predict success in a computer-based physics unit.
DISCUSSION
Review of Literature
Much of the research pertaining to correlating success or failure with certain variables was completed in the 1970s and 1980s. This may have been partially in response to high profile reports that linked student success with socio-economic background. In a review of the current literature of the time, Margrain (1978) concluded that little variance could be explained after accounting for general intelligence. Much of the research produced mixed results, as different variables accounted for different portions of the variances in achievement. Another difficulty in identifying a consistent set of variables was the wide variety of indicators of success used in the different studies as the dependent variable. The inclusion of variables outside the realm of the school, such as socio-economic and other background variables, also did not consistently explain student variances in success.
However, the large amount of still unaccounted for variance in student performance suggests that the students might not be the sole arbiters of their success. Their teachers' ability, personality, bias, methods, and numerous variables associated with the institution attended must also be conconsidered [sic] for complete and accurate prediction of academic performance. (Margrain, 1978, p.121)
Colleges and universities have also consistently used predictor variables to find attributes of students that can be used for admission policies. The most typical variables used for admission are standardized test scores, such as the ACT or SAT, and high school performance. These two variables, however, also show a discrepancy as to how well they predict the success of a student at a university. The highest levels of correlation are formed only under certain conditions. The students need to stay in dorms on campus, their freshman class has to have a relatively small enrollment (i.e., 500 or less), and have above average standardized test scores that have a large standard deviation (Munday, 1970). More recently, an examination of these same variables, standardized test scores and high school performance, found that they correlated well with students' academic performance at institutes of higher learning, but did not hold any predictive value for students' interest or enjoyment of their studies (Harackiewicz, Barron, Tauer, & Elliot, 2002).
Many departments and colleges within a university also use correlations to attempt to predict students' success in their programs and to determine at what level they should be placed within the program. A study of engineering students revealed that the single best variable for predicting success was math achievement (Levin & Wyckoff, 1988). In an effort to determine why only 40% of males and 33% of females persisted in the natural sciences through graduation, achievement in mathematics was again the single best indicator (Adair, 1991). The combined variable of high school GPA (grade point average) and ACT score was the best predictor of achievement in a series of college English and math courses (Noble & Sawyer, 1989). This correlation was then used to suggest models for placing incoming students into the math and English curriculum at the appropriate level.
When completing correlations or predictive models, one of the confounding variables is gender. In some cases gender correlates with success (Okpala & Onocha,...
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