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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34
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Item Association rules with SIA in B-Learning Courses: A mapping review(Editorial Virtual Argentina, 2017-04-20) Pazmiño-Maji, R. A,; García-Peñalvo, F. J.; Conde-González, M. Á.According to scopus between the years 2012 and 2016 there are 3556 scientific documents about Blended Learning, these have been and are still an emerging learning methodology. With this document, we determine the association rules with statistical implicative analysis (SIA) in B-Learning courses in Science Faculty at the ESPOCH University. To this end, we use mapping review in the blended learning courses used in the last 5 years (2012 to 2016) in Institutional platform, milaulas.com, and google. We started with 3350 B-Learning courses and finally 13 had all quality criteria. This document also describe a Institutional experience about Association rules with SIA in B-Learning Courses in the last five years.Item Comparing Hierarchical Trees in Statistical Implicative Analysis & Hierarchical Cluster in Learning Analytics(ACM, 2017-10-18) Pazmiño-Maji, R. A.; García-Peñalvo, F. J.; Conde-González, M. Á.Learning Analytics has been and is still an emerging technology in education; the amount of research on learning analysis is increasing every year. The integration of new open source tools, analysis methods, and other calculation options are important. This paper aims to compare hierarchical trees in Statistical Implicative Analysis (SIA) and some hierarchical clusters in Learning Analytics. To this end, we must use a quasi-experimental design with random binary data. A comparison is about the time it takes to evaluate the function for execute the four cluster algorithms: cohesion tree (ASI), similarity tree (ASI), agnes (cluster R package) and hclust (R base function). This paper provides an alternative hierarchical cluster used in Statistical Implicative Analysis that is possible to use in Learning Analytics (LA). Also, provides a comparative R-program used and identifies future research about software performance.Item Is it possible to apply statistical implicative analysis in hierarchical cluster analysis? Firsts issues and answers(2017) Pazmiño-Maji, R. A.; García-Peñalvo, Francisco J.; Conde-González, M. Á.Item Approximation of Statistical Implicative Analysis to Learning Analytics: A systematic review(2016-11) Pazmiño-Maji, R. A.; García-Peñalvo, Francisco J.; Conde-González, M. Á.Item Statistical implicative analysis for educational data sets: 2 analysis with RCHIC(2015-11) Coutrier, R.; Pazmiño, R.; Conde-González, M. Á.; García-Peñalvo, Francisco J.En este trabajo mediante dos ejemplos mostramos nuestro interés en la utilización del Análisis Estadístico Implicativo (SIA) en la comprensión de relaciones entre datos en Educación. Con SIA y la herramienta RCHIC es posible construir, gráficos (árbol de jerarquía, grafo implicativo) en los cuales el profesor o experto pueden visualizar y comprender las implicaciones entre los datos. Recomendamos a los profesores e instituciones utilizar SIA, debido a que ésta es una herramienta que permite encontrar posibles soluciones para mejorar evaluaciones, encuestas, etc.