Catherine Finnegan

Predicting Retention in Online General Education Courses

A classification rule was developed to predict undergraduate students& withdrawal from or completion of fully online general education courses. A multivariate technique, predictive discriminant analysis (PDA), was used. High school grade point average and SAT mathematics score were shown to be related to retention in the online university courses. Locus of control and financial aid were able to identify dropout and completion with 74.5% accuracy.

Best Practices in Predicting and Encouraging Student Persistence and Achievement Online

Four research studies of students and faculty engaged in fully online undergraduate courses are analyzed to generate best practices for teaching and learning online. These studies investigated the relationship of student background variables and online behaviors to student persistence and achievement in the online environment. Over 500 students enrolled in lower division, undergraduate courses offered online were included in the analyses. The courses were designed by faculty and instructional design experts and met standards of quality course design established by the offering colleges and universities. By combining the results of the study, guidelines for advising students and faculty for success in the online environment emerged.

Differences by Course Discipline on Student Behavior, Persistence, and Achievement in Online Courses of Undergraduate General Education

This research empirically examined student behavior in online courses and its relationship to persistence and achievement across fields. Eight variables descriptive of student behaviors online were measured for 1) frequency and 2) duration of participation. Twenty-two courses were grouped into three broad fields: English and Communication; Social Sciences; and Math, Science, and Technology. The descriptive data revealed significant differences in student online participation, persistence, and achievement across the fields. Multiple regression analyses were used to evaluate how well student participation measures predicted achievement. Depending on the field, different portions of the variability in achievement were accounted for by student participation measures.