EEXAMPRO: A web-based admission and program checker with machine learning

Authors

Charlie E. Pelingon CPSU (Central Philippines State University, Moises Padilla Campus, Moises Padilla, Negros Occidental, Philippines). Philippines
Jake R. Pomperada Technological University of the Philippines - Visayas, Talisay City, Negros Occidental, Philippines
Dennis V. Madrigal University of Negros Occidental-Recoletos, Bacolod City, Philippines

DOI:

https://doi.org/10.47577/technium.v30i.12954

Abstract

The increasing demand for an efficient and data-driven admission process has necessitated the development of eExamPro: A Web-Based Admission and Program Checker with Machine Learning. This study aims to modernize entrance examinations by automating test administration, real-time scoring, and personalized program recommendations using machine learning algorithms, improving decision-making for both students and institutions. The system provides a comprehensive platform for admissions management, integrating a secure offline examination system with a machine learning-driven course recommendation engine. It streamlines applicant evaluation by automating exam distribution, scoring, and result processing, ensuring consistency and reducing human error while offering institutions insights into student performance trends for data-driven decision-making. eExamPro features an adaptive testing mechanism, a personalized course suggestion system, and real-time data visualization tools for admissions officers. It allows applicants to take exams offline, ensuring accessibility. Administrators can monitor exam statistics, program demand, and student performance trends through dynamic dashboards, enabling improved institutional planning. The system's external interface includes a responsive web-based platform with multi-device compatibility. It supports user authentication, secure communication protocols, and seamless integration with institutional databases. Additionally, it provides customizable role-based access for administrators, program heads, and applicants, ensuring data privacy and a structured, efficient admissions workflow. Beyond functional requirements, eExamPro ensures high performance, security, and usability. It incorporates scalable infrastructure, automated backups, data encryption, and access control policies. The system adheres to educational data privacy regulations, maintaining audit trails and compliance measures for secure handling of student and institutional records. In conclusion, eExamPro revolutionizes the admission process, offering an intelligent, automated, and scalable solution for educational institutions. The system enhances efficiency, accuracy, and fairness in entrance examinations and program selection. Future enhancements include expanded AI capabilities for deeper academic analytics and integration with broader student information systems to support long-term academic planning.

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Published

2025-06-17

How to Cite

Pelingon, C. E., Pomperada, J. R., & Madrigal, D. V. (2025). EEXAMPRO: A web-based admission and program checker with machine learning. Technium: Romanian Journal of Applied Sciences and Technology, 30, 85–96. https://doi.org/10.47577/technium.v30i.12954