Describes a study of computer science majors conducted at Purdue University to determine what factors predict academic success or lack of it. Results show that strength in mathematical and verbal skills is necessary for success as indicated by SAT scores, minimum number of high school mathematics credits, and class standing. (MBR)

Reports the methodology and results of a research project which assessed the level of computer literacy among 119 education majors. The predictor variables studied were locus of control, age, sex, number of college math courses, and basic math skills. A questionnaire used in the survey is appended. (EAO)

Examines the effects of high school performance, high school and university mathematics background, previous computer experience, and age on computer science aptitude and achievement as measured by a computer science aptitude test and final exam respectively. Methodology and analysis of results by stepwise regression are presented. (EAO)

Designed to determine whether computer-assisted instruction could enhance performance on Piagetian classification and conservation tasks, this study divided 44 preschool children into an experimental group, which was exposed to computer lessons, and a control group. Both age and computer exposure were found to be significant variables. (LRW)

This meta analysis of 101 studies of computer-based education in colleges highlights examination performance, student attitudes toward instruction and computers, the relationship of publication source to study outcome, and time needed for instruction. One hundred twenty-one references are listed. (Author/LRW)

Reviews a study of 117 students at the University of Saskatchewan which examined the relationship between high school computer experience and university achievement in introductory computer science. The pretests used are described, findings are analyzed, and student withdrawal patterns are discussed. (Author/LRW)

Describes a study of 38 students at Eastern Oregon State College which identified mathematics proficiency as the key student characteristic leading to successful completion of computer science courses, and developed a model that accounts for 82 percent of the variation in students' final grades in computer courses for use by advisers. (LRW)