Year: 2025
Type: article
Abstract: Educational institutions are constantly seeking ways to optimize resource allocation, improve course scheduling, and ensure effective long-term planning. Anticipating learner enrolment trends is essential to achieve these goals. This paper introduces a comprehensive analysis of a Learner Enrolment Prediction System (LEPS) designed to accurately forecast future learner enrolment. LEPS utilizes a combination of historical enrolment data, demographic factors, socio-economic indicators, and academic performance metrics to identify patterns and project future enrolment trends. The system features a user-friendly interface, allowing administrators to input data, customize forecasting parameters, and visualize predictions through interactive dashboards and graphs. Furthermore, LEPS integrates a feedback mechanism to continuously update and refine its models, ensuring responsiveness to evolving educational trends. By providing data-driven insights into enrolment projections, LEPS enables institutions to make informed decisions regarding resource distribution, staffing, infrastructure expansion, and curriculum development. The implementation of LEPS has the potential to significantly enhance strategic management practices, leading to improved operational efficiency and overall institutional effectiveness.
Author: Nivedita Sinha
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4409082217