Content area
Developmental studies programs of higher education were established to help high-risk students learn successfully and acquire the basic skills of regular students. The purpose of this study is to explore the effects of developmental studies programs of higher education in Georgia. This study identifies the features of developmental studies programs and the traits of studies related to the effects of developmental studies programs.
Nineteen studies which deal with the effects research of developmental studies programs of higher education in Georgia are collected. Meta-analysis is employed as the methodology of this study. These studies are coded by statistical data, the characteristics of study methods, and the characteristics of developmental studies programs. Ten features are created as the study independent variables, and the relationship between these independent variables and the effects of developmental studies programs are analyzed.
The t-test, linear regression and multiple regression are used to analyze the relationship between experimental and the comparison groups and the effects of ten features on the effects. LSD (least significant difference) posteriorio is employed to analyze the differences among the levels of each resulting significantly different feature.
The study results report that generally developmental studies programs are effective. They benefit high-risk students in college learning. Developmental studies programs improve high-risk students' learning in mathematics, GPA, and affective domain. Yet in the categories of English and achievement, this study shows that developmental studies programs do not improve high-risk students' learning.
Summarizing the results, this study finds that if the effects researcher of developmental studies programs can create experimental and comparison groups by equalizing their basic skills; randomly assigning them to the treatment; including the affective instruction in academic courses; using standardized tests with SAT and HSGPA to identify the high-risk students; employing t-test, chi-square, ANOVA, or ANCOVA to analyze data; adopting the experimental model such as the Solomon design or the pre- posttest and random control design; he will make the programs' effects visible.
Finally, a model for evaluating developmental studies programs is suggested based on the Stufflebeam' CIPP model.