This applied dissertation was designed to identify the characteristics of students enrolled in a GED® preparation program who transitioned to postsecondary programs at the same institution after passing the GED® test. The characteristics studied included age; gender; ethnicity; prematriculation scores in reading, language, and math in the Test of Adult Basic Education (TABE); and hours spent preparing for the GED® test in an open-entry, open-exit remedial laboratory environment.
Through the use of binary logistic regressions to answer the research questions, a prediction model was constructed. The variables that are able to predict an increased likelihood of transition to postsecondary programs were being between the ages of 16 and 24 at the time of enrollment in the GED® program and having an ethnicity category of Asian, White/Caucasian, Hispanic, or Black/African American as opposed to the category of No Report. The variables that significantly predicted a lessened likelihood of transition to postsecondary programs were a grade equivalent of less than 8.9 in the prematriculation TABE reading, language, and math scores. Spending less than 16 hours preparing for the GED ® test was also found to lessen the likelihood of transition.
The findings of this study are important to adult education practitioners, tutors, teachers, and administrators who are responsible for GED® programs. Through application of the prediction model in a similar environment, supportive and interventional mechanisms can be created to increase the number of GED® earners who transition to credit, college preparation, and vocational programs.
|Commitee:||Thompson, Thomas J.|
|School:||Nova Southeastern University|
|Department:||Abraham S Fischler School of Education|
|School Location:||United States -- Florida|
|Source:||DAI-A 76/04(E), Dissertation Abstracts International|
|Subjects:||Educational evaluation, Adult education, Secondary education|
|Keywords:||College bound students, College preparation, Dropout programs, High school equivalency programs, Predictive measurement|
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