Bayesian Network based Elucidatory Model of Change for Flexible Academic System in Post- Graduate Classes

The purpose of this study is to apply Bayesian Network on change model that supports increase in number of graduate students in post-graduate classes. Change is the process of shifting from current position to desired position. Change can be brought from two modes i. e. behavioral and structural. Three main constraints that confront decision making by a graduate for getting enrolled in post-graduate studies include (i) Non-Readiness, (ii) Rigidity of Academic System and (iii) Shortage of Resources. Nonetheless, three core variables that counter constraints include (i) Readiness that is parenting Awareness Program and Student Counseling (ii) Flexible Academic System that is parenting Distance Learning Program and Flexible Schedule of Classes and (iii) Provision of Resources that is parenting Scholarship Program and Higher-Education Allowance for Parents. A survey feedback of 121 respondent (graduate and not studying further) from Karachi represents that "Flexible Academic System" is more likely to be considered while making decision for getting admission in post-grad studies. Cumulative causal effect of "Flexible Academic System" to the dependent variable "Admissions in Post-Grad Programs" is 0. 4, "Readiness" has 0. 19 and "Provision of Resources" has 0. 18. Cumulative causal effect of entire model is 0. 77 that seems fruitful, if executes. In this context, government and NGOs should work on Readiness and Provision of Resources while universities should offer Flexible Academic System in order to increase number of students in post-grad classes.