Mathematical Models for Module-Specific Student Forecasting and Workload Optimization: A Basis for Higher Education Operations Management

Abstract

In a fast-changing landscape of higher education, accurate module-specific student forecasting is of great value for efficient resource allocation and effective operations management. This paper embarks on the development of Mathematical models for student forecasting and optimizing staff workload, leveraging the computational power of Advanced MS Excel with Macros and Visual Basic Applications (VBA) and the data management abilities of MS Access. Through an exploratory-sequential approach coupled with data mining, this study aims to provide Higher Education Institutions (HEIs) with a robust and adaptable forecasting tool. The research realizes the multi-faceted and dynamic nature of enrolment trends, which portray complex patterns influenced by various factors or variables. This study found that the developed Mathematical models involving Cumulative Frequency (Ogive function), Summation, and Piecewise-defined Function integrated in both MS Excel and MS Access demonstrated a high level of accuracy, around 97%, enabling the institution considered to optimize its workload effectively and efficiently. This resulted in a more balanced allocation of resources, avoiding understaffing and overstaffing issues. By aligning staffing levels with actual forecasts, the institution benefited in terms of significant costs savings. Moreover, the study found that optimizing workload had a positive impact on both the student and staff satisfaction by improving workload fairness and timetables. Additionally, the showcased models found to be scalable for other departments and different numbers of students. The study’s findings have significant policy implications for HEI governance. Institutional leaders can consider adopting the Mathematical Models to improve their resource allocation efficiency.