Academic results meanings with data mining mediated by the use of serious video game matelogic

Fabiola Talavera Mendoza, Hugo Rucano Paucar, Klinge Villalba Condori

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this article we argue that datification can offer new opportunities and roles for education in gathering evidence for timely teacher intervention. It is intended to analyze data attributes through grouping algorithms and classification as data mining tools related to the progress of academic performance through the learning pathways of students before and after using the SG Matelogic. The research is quantitative, with an experimental study, using data from 25 participants from the fourth grade of primary education. The main finding obtained in the application of the algorithms K-means and Random Tree allowed a progressive achievement of the learning, from the interaction with the serious video game. It is concluded that the application of data mining allows to determine a pattern of interaction to inform and support students and teachers to reflect and self-regulate learning achieving a level of impact of the serious game Matelogic in the school curriculum.

Original languageEnglish
Pages (from-to)509-518
Number of pages10
JournalJournal of Advanced Research in Dynamical and Control Systems
Volume11
Issue number11 Special Issue
DOIs
StatePublished - 2019
Externally publishedYes

Keywords

  • Academic performance
  • Attributes
  • Automatic learning
  • Learning itineraries

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