Adaptable Data Warehouse Based on the Research Factor of the NAC Institutional Accreditation Model

Author:

Fuentes-Vargas David-AntonioORCID,Mendoza-Becerra Martha-ElianaORCID,Gómez-Flórez Luis-CarlosORCID

Abstract

One of the main challenges of higher education institutions is continuously improving educational quality. In Colombia, the National Accreditation Council is in charge of evaluating if an institution provides high-quality education. One of the stages in obtaining recognition of high quality requires submitting a self-assessment report with quantitative data by the institution. This stage is very demanding for the institutions because it requires handling data extracted from various sources. Data warehouses are an alternative solution since they allow information from various sources to be centralized and support decision-making. This article proposes dimensional models adaptable to the availability of information sources for institutions and focuses on investigative processes. The research methodology used is the Iterative Research Pattern, where the problem was observed through the review of related studies and self-assessment reports submitted to the National Accreditation Council by public institutions. Subsequently, the requirements of the model were created and validated by a group of experts in institutional quality accreditation. Then, the solution was developed, and six adaptable dimensional research models were proposed using the MiPymes methodology, which is validated through a focus group of experts in dimensional modeling of data warehouses that considered the degree of adaptability of the models is 100% to the identified requirements.

Publisher

Universidad Pedagogica y Tecnologica de Colombia

Subject

Materials Science (miscellaneous)

Reference17 articles.

1. CESU, Acuerdo 03 de 2014, Consejo Nacional de Acreditación (NAC). Colombia, p. 32, 2014.

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3. G. E. Silva Peñafiel, V. M. Zapata Yánez, K. P. Morales Guamán, L. M. Toaquiza Padilla, “Análisis de metodologías para desarrollar Data Warehouse aplicado a la toma de decisiones,” Ciencia Digital, vol. 3, no. 3.4., pp. 397–418, 2019. https://doi.org/10.33262/cienciadigital.v3i3.4..922

4. IBM, Engineering Lifecycle Management - Data Warehouse, 2022. https://www.ibm.com/docs/en/elm/6.0?topic=overview-data-warehouse

5. R. Kimball, M. Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3d ed. John Wiley & Sons, Inc., 2013.

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