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A Business Intelligence Framework for Analyzing Educational Data

Author

Listed:
  • William Villegas-Ch

    (Escuela de Ingeniería en Tecnologías de la Información, FICA, Universidad de Las Américas, 170125 Quito, Ecuador)

  • Xavier Palacios-Pacheco

    (Departamento de Sistemas, Universidad Internacional del Ecuador, 170411 Quito, Ecuador)

  • Sergio Luján-Mora

    (Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, 03690 Alicante, Spain)

Abstract

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university.

Suggested Citation

  • William Villegas-Ch & Xavier Palacios-Pacheco & Sergio Luján-Mora, 2020. "A Business Intelligence Framework for Analyzing Educational Data," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5745-:d:385719
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    References listed on IDEAS

    as
    1. Mohamad Hamed & Tariq Mahmoud & Jorge Marx Gómez & Georges Kfouri, 2017. "Using Data Mining and Business Intelligence to Develop Decision Support Systems in Arabic Higher Education Institutions," Springer Proceedings in Business and Economics, in: Jorge Marx Gómez & Marie K. Aboujaoude & Khalil Feghali & Tariq Mahmoud (ed.), Modernizing Academic Teaching and Research in Business and Economics, pages 71-84, Springer.
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    Citations

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    Cited by:

    1. Piotr Muryjas & Monika Wawer & Magdalena Rzemieniak, 2021. "Managing the Process of Evaluation of the Academic Teachers with the Use of Data Mart and Business Intelligence," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 127-140.
    2. Mihaela Muntean & Doina Dănăiaţă & Luminiţa Hurbean & Cornelia Jude, 2021. "A Business Intelligence & Analytics Framework for Clean and Affordable Energy Data Analysis," Sustainability, MDPI, vol. 13(2), pages 1-25, January.
    3. Marco Nunes & António Abreu & Célia Saraiva, 2021. "A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business," Sustainability, MDPI, vol. 13(11), pages 1-28, May.

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