Analysis of the state of learning in university students with the use of a hadoop framework

William Villegas-Ch, Milton Roman-Cañizares, Santiago Sánchez-Viteri, Joselin García-Ortiz, Walter Gaibor-Naranjo

Research output: Contribution to journalArticlepeer-review

Abstract

Currently, education is going through a critical moment due to the 2019 coronavirus disease that has been declared a pandemic. This has forced many organizations to undergo a significant transformation, rethinking key elements of their processes and the use of technology to maintain operations. The continuity of education has become dependent on technological tools, as well as on the ability of universities to cope with a precipitous transition to a remote educational model. That has generated problems that affect student learning. This work proposes the implementation of a Big Data framework to identify the factors that affect student performance and decision-making toimprove learning. Similar works cover two main research topics under Big Data in education, the modeling and storage of educational data. However, they do not consider issues such as student performance and the improvement of the educational system with the integration of Big Data. In addition, this work provides a guide for future studies and highlights new insights and directions for the successful use of Big Data in education. Real-world data were collected for the evaluation of the proposed framework, the collection of these being the existing limitation in all research due to generalized rejection of data consent.

Original languageEnglish
Article number140
JournalFuture Internet
Volume13
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Analysis of data
  • Hadoop
  • Learning analytics

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