A Prototype for Educational Planning Using Course Constraints to Simulate Student Populations

TL;DR

This paper presents a prototype system that uses course constraints and Markov models to simulate and estimate student populations in distance learning programs, aiding academic planning and resource allocation.

Contribution

It introduces a novel method combining course precedence constraints with Markov models to predict student population fluctuations in distance learning environments.

Findings

Effective estimation of student populations using the proposed model

Identification of key issues for large-scale deployment

Potential to improve resource planning and academic viability

Abstract

Distance learning universities usually afford their students the flexibility to advance their studies at their own pace. This can lead to a considerable fluctuation of student populations within a program's courses, possibly affecting the academic viability of a program as well as the related required resources. Providing a method that estimates this population could be of substantial help to university management and academic personnel. We describe how to use course precedence constraints to calculate alternative tuition paths and then use Markov models to estimate future populations. In doing so, we identify key issues of a large scale potential deployment.