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Title: Comparing Hierarchical Trees in Statistical Implicative Analysis & Hierarchical Cluster in Learning Analytics
Authors: Pazmiño-Maji, R. A.
García-Peñalvo, F. J.
Conde-González, M. Á.
Keywords: Clustering
Software performance
Learning analytics
statistical implicative analysis
Open source software
hierarchical cluster
similarity tree
Issue Date: 18-Oct-2017
Publisher: ACM
Citation: Pazmiño-Maji, R. A., García-Peñalvo, F. J., & Conde-González, M. Á. (2017). Comparing Hierarchical Trees in Statistical Implicative Analysis & Hierarchical Cluster in Learning Analytics. In J. M. Dodero, M. S. Ibarra Sáiz, & I. Ruiz Rube (Eds.), Fifth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’17) (Cádiz, Spain, October 18-20, 2017) (Article 49). New York, NY, USA: ACM. doi:10.1145/3144826.3145399
Abstract: 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.
ISBN: 10.1145/3144826.3145399
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