Contributions of Data Mining to University Education, in the Context of the Covid-19 Pandemic: A Systematic Review of the Literature

Martín Díaz-Choque, Omar Chamorro-Atalaya, Orlando Adrian Ortega-Galicio, José Antonio Arévalo-Tuesta, Elvira Cáceres-Cayllahua, Ronald Fernando Dávila-Laguna, Irma Esperanza Aybar-Bellido, Yina Betty Siguas-Jerónimo

Research output: Contribution to journalArticlepeer-review


During the context of COVID-19, educational processes migrated to a strictly virtual scenario, so the quantity of information grew in such a way that techniques such as data mining or machine learning contributed to generating knowledge for decision-making. In this sense, it is relevant to define the state of the art of the contributions of data mining in the university environment, and from there, to see in perspective how these could be applied in scenarios of return to the face-to-face. In this sense, a systematic review of the literature is carried out, based on scientific evidence extracted from the Taylor & Francis, ERIC and Scopus databases. A qualitative content analysis approach and the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement were used to extract the findings published in scientific articles. The results were that educational data mining was applied to a greater extent in the field of “teaching”, and it was focused on the search for patterns and predictive models to improve student performance, reduce student dropout, improve the student’s quality of life, and teacher performance. In addition, as a resource for data extraction, university learning management systems (LMS) were used to a greater extent. It is concluded that tools such as data mining should be implemented as academic management policies, achieving a prospective on indicators linked to the improvement of student learning and performance.

Original languageEnglish
Pages (from-to)16-33
Number of pages18
JournalInternational journal of online and biomedical engineering
Issue number12
StatePublished - 2023
Externally publishedYes

Bibliographical note

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© 2023 by the authors of this article. Published under CC-BY


  • COVID-19
  • data mining
  • higher education
  • systematic review


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