Bayesian Analysis of Retention and Graduation of Female Students of Higher Education Institution: The Case of Hawassa University (HU), Ethiopia

Abstract

The study was conducted on female students who were 2005, 2006, 2007, and 2008 entries in the fields of Natural Science, Agriculture, and Social Science. From 1931 female students a sample of 605 was taken using stratified random sampling, Primary and secondary data were collected using questionnaire and analyzed using the Bayesian logistic regression analysis. The results showed that the percentage of graduation among 362 females who were enrolled in 2005, 2006, and 2007 was 72.1%. Similarly the retention rate among 243 females of 2008 entry was 75.7%. From the Bayesian logistic regression analyses, significant predicators of both graduation and retention were choice of field, preparatory average result, entrance exam score and first year cumulative GPA. Moreover pregnancy, organizing studying and leisure time, habit of chewing Khat, satisfaction with instructors, parent income, habit of smoking cigarette and using drugs, and feel safe to study at night in classrooms appeared as significant predictors of retention. The graduation rate and retention rate for the students who assigned to the field they did not choose were lower than that for those assigned to the field they chose. Those with first year CGPA less than 2.0 were having lower rates of graduation and retention than those having greater than 2.0. The graduation and retention rates for the students having higher preparatory average result and higher entrance exam score were higher than that for those having lower. The students having parents' income less than 500 were less likely to retain than those having parents' income greater than 1500. The retention rate for the students who were not satisfied with their instructors was lower than those were satisfied. The students who cannot organize their study and leisure time easily were less likely to retain than those can organize. In conclusion, the factors those mainly affect female students' graduation and retention were more of academic variables; hence we recommend that assigning to the field they choose by their interest may help female students' graduation and retention. The teaching method at secondary and preparatory schools should be designed to challenge and motivate them to adequately prepare them for Higher Education Institutions. Moreover, campus and Department administrators in collaboration with the students themselves and academic staff need to work hard to bring change in behavior, academics, and social aspects of female students at the University.

An application of Bayesian inference to examine student retention and attrition in the STEM classroom

“…Despite their alignment with these four ML assessment criteria, compared to the use of traditional statistical methodologies, Bayesian ML methods are an underrepresented and underutilized statistical methodology employed in education research (Subbiah et al, 2011;König and van de Schoot, 2018). A limited amount of work in the literature has used Bayesian techniques to understand the factors impacting student performance such as the grade point average (GPA) of college students (Hien and Haddawy, 2007), graduation rates (Crisp et al, 2018;Gebretekle and Goshu, 2019), and final examination performance (Ayers and Junker, 2006). Even less work has focused on quantitatively assessing the impact of different data types on student performance outcomes.…”