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

David-Antonio Fuentes-Vargas, Martha-Eliana Mendoza-Becerra, Luis-Carlos Gómez-Flórez
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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.
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基于NAC机构认证模型研究因素的适应性数据仓库
不断提高教育质量是高等教育面临的主要挑战之一。在哥伦比亚,国家认证委员会负责评估一个机构是否提供高质量的教育。获得高质量认可的其中一个阶段要求院校提交一份载有定量数据的自我评估报告。这一阶段对机构的要求非常高,因为它需要处理从各种来源提取的数据。数据仓库是一种替代解决方案,因为它们允许集中来自不同来源的信息并支持决策。本文提出了适应机构信息源可用性的维度模型,并着重于调查过程。采用的研究方法是迭代研究模式,通过审查相关研究和公共机构向国家认可委员会提交的自我评估报告来观察问题。随后,一组机构质量认证专家创建并验证了该模型的要求。然后,开发了该解决方案,并使用MiPymes方法提出了六个可适应的维度研究模型,并通过数据仓库维度建模专家焦点小组进行了验证,该小组认为模型对确定的需求的适应性程度为100%。
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