AVIN: A Case Study on a Supporting Tool for the Self-Assessment Process in Higher Education Institutions

• Tiago Brasileiro Araújo IFPB
• Iriedson Souto Maior de Moraes Vilar IFPB
• Gilvonaldo Alves da Silva Cavalcanti IFPB
• Ana Maria Alves Felix IFPB
• Emerson Andrey Fausto dos Santos IFPB
• Daniel dos Santos Costa IFPB
• Rayane da Silva Rodrigues IFPB

DOI: https://doi.org/10.5753/semish.2025.7317

Abstract

Brazilian Higher Education Institutions (HEIs) periodically undergo self-assessment processes to identify weaknesses and opportunities for improvement. These evaluations help analyze issues such as dropout rates, inadequate infrastructure, and student disengagement, supporting the development of strategic actions. This paper presents AVIN, a tool designed to facilitate this process by leveraging Business Intelligence (BI) concepts. AVIN enables HEIs to collect, process, and visualize data efficiently, providing reliable insights for decision-making. The tool aligns with the Ministry of Education self-assessment framework, integrating institutional participant groups to ensure accurate data retrieval and visualization.

References

Ain, N., Vaia, G., DeLone, W. H., and Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success–a systematic literature review. Decision Support Systems, 125:113113.

da Silva, G. A. C., Alves, N. R., de Farias Amorim, A., Sousa, P. H., Araujo, T. B., and de Lima, G. A. N. (2022). Aplicação de business intelligence no processo de autoavaliação de instituições de ensino superior. In Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 1–4. SBC.

Ghouri, A. M. and Mani, V. (2019). Role of real-time information-sharing through saas: An industry 4.0 perspective. International Journal of Information Management, 49:301–315.

Ibrahim, A. M. A., Abdullah, N. S., and Bahari, M. (2023). Software as a service challenges: A systematic literature review. In Proceedings of the Future Technologies Conference, pages 257–272. Springer.

Khan, M. A. and Khojah, M. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of saudi arabia. Education Research International, 2022:1–10.

Lavalle, A., Maté, A., Trujillo, J., and Rizzi, S. (2019). Visualization requirements for business intelligence analytics: a goal-based, iterative framework. In 2019 IEEE 27th International Requirements Engineering Conference (RE), pages 109–119. IEEE.

Lima, G., Araújo, T., Azevedo, L., and Neto, F. (2019). Um metamodelo para elaboração, aplicação e análise de autoavaliações institucionais em conformidade com o sinaes. Revista Principia - Divulgação Científica e Tecnológica do IFPB, 1(44):122–131.

Macedo, A., Silva, A., and Borba de Arruda, A. L. (2017). Avaliação da educação superior no brasil: discursos, práticas e disputas. Revista Práxis Educativa, 12(3).

Maia, A., Portela, F., and Santos, M. F. (2018). Web intelligence in higher education: A study on the usage of business intelligence techniques in education. In 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pages 176–181. IEEE.

Ong, V. K. (2016). Business intelligence and big data analytics for higher education: Cases from uk higher education institutions. Information Engineering Express, 2(1):65–75.

Santi, R. P. and Putra, H. (2018). A systematic literature review of business intelligence technology, contribution and application for higher education. In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), pages 404–409. IEEE.

Seifert, M., Kuehnel, S., and Sackmann, S. (2023). Hybrid clouds arising from software as a service adoption: challenges, solutions, and future research directions. ACM Computing Surveys, 55(11):1–35.

Stavrinides, G. L. and Karatza, H. D. (2020). Scheduling real-time bag-of-tasks applications with approximate computations in saas clouds. Concurrency and computation: Practice and experience, 32(1):e4208.