Statistical techniques and learning achievement: literature review

Técnicas estadísticas y logro de aprendizaje: revisión bibliográfica

Main Article Content

Lilian Daniela Suárez Riveros
Wilmer Pineda Ríos
Iván Mauricio Mendivelso Ramírez
Abstract

The aim of this document was to describe the different statistical
techniques that have been used to understand or explain the achievement of learning
in Students at different educational levels. From the theoretical point of view, the a
priori categories from statistical techniques were consolidated (Multilevel Models,
Geospatial Models, Regression, Clustering, Descriptive Analysis, Neural Networks,
Decision Trees, Random Forests, Naive Bayes and Support Vector Machine) as well
as the conceptualization of Learning Achievement. The methodological approach for
the review was based on the bibliographic informative mapping. Among the results
are 50 documents from different databases (Elsevier (1), Google Scholar (6), IEEE
(4), Scielo (2), Science Direct (5), Scopus (31), y Springer (1)), who study different
regions of the world (Asia (17), South America (13), North America (8), Europe (6),
Africa (5), Oceania (4), Central America (3)), along with the orientation to explain
(17), understand (31) or understand and explain (2). Additionally, they identified a set
of emerging variables in the different reports, among which the socioeconomic level,
gender, affectivity, background and characteristics and possibilities of the parents are
most relevant.

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