predictive model of student performance in Internet-based distance learning courses at the community college

The purpose of this research study was to develop a predictive model of student performance in Internet-based distance learning courses at the community college level. The predictor variables included socioeconomic status as it relates to age, gender, marital status, income, and race, as well as, level of education, computer proficiency, motivation, academic support, and grade received in the course. The survey used in this study was the Internet Based Distance Learning Courses Questionnaire (IBDLQ). The survey was administered to a sample of 291 completers of Internet-based distance learning courses at the end of the Summer 2000 and Fall 2000 school semesters at Palm Beach Community College. One hundred respondents returned completed surveys, indicating a return rate of 34%. Multiple linear regression analysis was used to test each hypothesis and to provide a model that was predictive of student performance. Nine null hypotheses were formed to determine if there were significant relationships between student performance and the aforementioned variables. The results of the tests of the nine null hypotheses showed that the hypotheses that involved student performance and marital status, age and motivation-self pace were rejected. In this study, the final model indicated that the predictor variables accounted for 14.2% of the variance in student performance. The correlation matrix showed that the older students in this population were less often currently married than were younger students and appeared only marginally less likely to be motivated by self-paced courses. The correlation between being motivated by self-paced courses and being married showed that married students were a little more likely to be motivated by self-paced courses. Analysis of responses to the open-ended question on course satisfaction revealed four main themes that influence student performance: academic support from the instructor, flexibility, socioeconomic status specific to family responsibilities that include marital status, whether or not the student has dependents, and age. Suggestions for future research included increasing sample size, adding variables such as frequency of student computer use, whether or not the respondent has dependents, and surveying the instructors of the courses for frequency of availability online, levels of expertise, and instructor perception of barriers.