A Development of a Web And Mobile Application for a Faculty Ranking System With Data Analytics at National University Philippines
The Information and Communication Technology (ICT) accelerated and innovated several administrative processes in the educational settings. This includes faculty ranking and evaluation. In this study, the development of a web and mobile application for automating the process of re-ranking faculty members of National University in the Philippines is focused. The purpose of the study is to address the inefficiency and accuracy of the current manual re-ranking process. The mentioned manual process is time-consuming and riddled with errors due to file duplication and unruly storage systems. Previous studies pointed out the use of technology in the educational industry but did not provide enough evidence how the faculty re-ranking process can be automated. This research, therefore, will fill this gap that can ease and secure faculty credential management by creating a system. This will enhance decision-making via data analytics and can improve workflow efficiency in the Faculty Service Office. There will be two methodologies that will be used for this study: Descriptive and Agile. This will be used together to identify the current challenges using interviews and surveys. This will be followed by an Agile Kanban Framework application in the development of the application. The findings indicate that the proposed system significantly reduces processing time, minimizes errors, and provides insights through data analytics to ensure a more effective and efficient way of faculty management. It shows that any educational institution will perform better if it automates its administrative process for proper utilization of its resources.
Authors
- Eliseo Q. Ramirez (ORCID: https://orcid.org/0009-0003-1115-2456)
- Constanza Villamizar Luna
- Michael Vinz T. Balisi
- Kenneth G. Pingol
- Mc Stalin D. Villalobos
Institutions
- National University (PH)
- University of the Philippines Manila (PH)
Publication Details
- Published
- 2025-04-26
- DOI
- https://doi.org/10.1109/ic4e65071.2025.11075508
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00