Embedding Logistic Regression Model in Decision Support Software for Student Graduation Prediction

Logistic regression is a predictive modeling technique that finds an association between the independent variables and the logarithm of the odds of a categorical response variable. This is one of the techniques used in analyzing a categorical dependent variable. The study focused on the application of logistic regression in predicting student graduation by generating data models that could early predict and identify students who are prone to not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The student graduation rate is the percentage of a school’s first-time, first-year undergraduate students who complete their program successfully. Most students’ first-year freshmen enrolled at the tertiary level failed to graduate. According to the National Center for Education Statistics, almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate. The colleges and universities consisting of high leaver rates go through a loss of fees and potential alumni contributors.