A Logistic Regression Model to Evaluate Learning in a Semester System of Education Based on Classical and Objective Bayesian Approaches

This study models the impact of factors influencing the level of learning in semester system of education in Pakistan using the logistic regression model. The model is evaluated in classical and Bayesian frameworks. Maximum likelihood estimates of the model parameters are evaluated along with their standard errors and the significance of the estimate are reported therein. To keep the results using non-informative priors, we have used uniform prior to let the data speak for themselves. Marginal posterior distributions of the model parameters are also evaluated and discussed. The odd ratios of the response variable are computed to assess the impact of unit change in a particular explanatory variable or covariate on the learning probability of the students while keeping other covariates fixed. The goodness of the model is examined. It is observed that level of learning increases by an increase in satisfaction, marks in previous exams, class level, type of institution, regularity in taking classes, study hours per day and use of technology. Whereas demographic background, marital status, disease, residence mode, father’s qualification, family problems, and parents’ involvement in student’s study casts an adverse effect on the learning level. The model is proved to give good fit to the data.