A Data-Driven Web-Based System for Regional Program Accreditation: The Case of Inter-University Council for East Africa (IUCEA)

Carmel Nkeshimana (), Ben Ruhinda (), Devotha G. Nyambo () and Shubi F. Kaijage ()
Additional contact information
Carmel Nkeshimana: The Nelson Mandela African Institution of Science and Technology (NM-AIST), Computational and Communication Science and Engineering (CoCSE)
Ben Ruhinda: Inter-University Council for East Africa
Devotha G. Nyambo: The Nelson Mandela African Institution of Science and Technology (NM-AIST), Computational and Communication Science and Engineering (CoCSE)
Shubi F. Kaijage: The Nelson Mandela African Institution of Science and Technology (NM-AIST), Computational and Communication Science and Engineering (CoCSE)

A chapter in Advancement in Embedded and Mobile Systems, 2026, pp 17-35 from Springer

Abstract: Abstract In response to the evolving landscape of regional program accreditation, we undertook the development of a user-centric Regional Program Accreditation System (RPAS). This system was meticulously designed to cater to the diverse needs of accreditation bodies, educational institutions, and expert reviewers within the East African Region. Our journey commenced with an extensive requirement-gathering phase, engaging end-users to shape the system's design and functionality. The RPAS was built using cutting-edge technologies, including ReactJs, Django, API integration, Tailwind CSS, and MySQL, ensuring scalability, security, and flexibility. Agile methodologies, particularly Extreme Programming, were used, enabling expedited development and fostering close collaboration, ensuring adaptability to users’ evolving needs. The RPAS streamlines the accreditation process with a user-friendly interface and real-time collaboration features, thereby enhancing transparency and operational efficiency. Robust authentication mechanisms were implemented to ensure data security. Additional user-friendly features, such as night mode, QR code-based certificate verification, and automated notifications, were introduced for convenience. In the future, RPAS can be enhanced by implementing Machine Learning for Document Content Analysis, enhancing Reporting and Analytics Capabilities, and establishing Automated Reminders and Notifications. These enhancements will further empower RPAS, making it a valuable tool for streamlining accreditation processes and contributing to the elevation of program quality and educational excellence in the region.

Keywords: Process automation; Regional accreditation; Data-driven; Programme accreditation (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-99219-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783031992193

DOI: 10.1007/978-3-031-99219-3_2

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().