Abstract
Educational success measured by retention leading to graduation is an essential component of any academic institution. As such, identifying the factors that contribute significantly to success and addressing those factors that result in poor performances are important exercises. By success, we mean obtaining a semester GPA of 3.0 or better and a GPA of 2.0 or better. We identified these factors and related challenges through analytical models based on student performance. A large dataset obtained from a large state university over three consecutive semesters was utilized. At each semester, GPAs were nested within students and students were taking classes from multiple instructors and pursuing a specific major. Thus, we used multiple membership multiple classification (MMMC) Bayesian logistic regression models with random effects for instructors and majors to model success. The complexity of the analysis due to multiple membership modeling and a large number of random effects necessitated the use of Bayesian analysis. These Bayesian models identified factors affecting academic performance of college students while accounting for university instructors and majors as random effects. In particular, the models adjust for residency status, academic level, number of classes, student athletes, and disability residence services. Instructors and majors accounted for a significant proportion of students’ academic success, and served as key indicators of retention and graduation rates. They are embedded within the processes of university recruitment and competition for the best students.
Highlights
At institutions of higher education, undergraduate student academic performance is a major concern for the administration
Bayesian multiple membership logistic regression model monitoring programs have benefitted from the aid of predictive modeling and the use of random effects to account for the unmeasurable effects [1]
We investigated the impact of several student characteristics that are not normally used when modeling college student academic success, such as out-of-state, in-state or international residency status, simultaneously
Summary
At institutions of higher education, undergraduate student academic performance is a major concern for the administration. The hierarchical structure brings a measure of the intraclass correlation at each level of the hierarchy Such is certainly the case with the three consecutive semesters of university performance data. The standard logistic regression model is not appropriate It ignores the intraclass correlation inherent due to the multilevel structure. The data structure demands a model that incorporates the correlation due to the hierarchical structure of the data and the multiple memberships Such multilevel structure is common in many fields of research, especially education. This paper presents a fit of MMMC logistic regression models Such models allow one to account for multiple sources of variation due to the multiple levels of the hierarchy that may impact the response but are not directly measured [17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
