Statistical Modeling of Students’ Academic Performances: A Longitudinal Study

Abstract

Within students’ attrition studies, it is necessary to assess the longitudinal evolution of students within a given course of study, from enrolment to exit from the university through degree completion and academic dropout. Here, the student's academic progress is monitored through the number of courses failed each semester enrolled. The students’ failure rate and academic behavior typically provide significant insight into students’ exit outcomes from University programs. These programs usually have a maximum time frame required to complete the course. A likelihood-based approach is discussed that conditions on the exit outcome and random effects in adjusting within-subject correlation of longitudinal measurements. Ignoring the number of courses enrolled by a student may produce inadequate results on the actual failure rates. Conditioned on the exit outcomes of the student, we find out that factors such as financial aid, matriculation points, students’ race and course type registered, and gender are distinguishing factors that affect students’ academic performances, for completers and dropouts. Also, being in university-type accommodation (that often have added services such as transportation and internet connections) does not seem to significantly affect the failure rate within both groups of students. In addition, an increase in matriculation points significantly reduces the failure rate independent of the Quintile school of the student. Several count models such as mixed Poisson, mixed Zero Inflation Poisson, mixed Negative Binomial, and mixed Hurdle Poisson models are fitted and compared. In particular, the mixed Poisson model provides a better fit based on the Bayesian Information Criterion and residues analysis.