Learning analytics in Ecuador: a systematic review supported by statistical implicative analysis


Abstract:

Five thousand six hundred ten scientific articles related to Learning Analytics (LA) are stored in Scopus and Web of Science from 2014 until 2019, as evidence of the importance and increasing interest in this new line of research. LA is the measurement, collection, analysis and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs. LA is widely used in universities; Ecuador has three hundred forty-five Higher Educational Institutions. One of the reasons why research in the area of LA does not increase is the lack of a baseline (cutting edge information that provides starting points for researching and publishing). This research discovers and analyses scientific documents in LA and answers the central question: What is the baseline of scientific documents on LA in Ecuador? The methodology used was a Systematic Review (SR) to answer eight research questions about scientific articles and theses in LA by Ecuadorian authors. The study presented in this paper was carried out from 2014 to June 2019; ninety-nine scientific documents about LA were found in Scopus, WOS, IEEE, RRAAE and Senescyt. Sixty-one scientific documents were downloaded, arranged and analyzed using Statistical Implicative Analysis (SIA) after removing duplicates, applying inclusion, exclusion and quality criteria. SIA was proposed by Regis Gras and discovers rules between variables and represents them through dendrograms. SR together with SIA was used for the first time in this paper. The built baseline will allow learning more about LA in Ecuador and increasing research and publications.

Año de publicación:

2021

Keywords:

  • Systematic review, Ecuador
  • Statistical implicative analysis
  • learning analytics

Fuente:

scopusscopus
googlegoogle

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Análisis de datos

Áreas temáticas:

  • Ciencias de la computación
  • Educación
  • Tecnología (Ciencias aplicadas)