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    Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets

    Autor: 
    Martínez Navarro, Álvaro
    ;
    Moreno-Ger, Pablo (1)
    Fecha: 
    09/2018
    Palabra clave: 
    clustering methods; computer languages; data analysis; engineering students; performance evaluation; unsupervised learning; Emerging
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/7681
    DOI: 
    https://doi.org/10.9781/ijimai.2018.02.003
    Dirección web: 
    http://www.ijimai.org/journal/node/2111
    Open Access
    Resumen:
    Learning Analytics is becoming a key tool for the analysis and improvement of digital education processes, and its potential benefit grows with the size of the student cohorts generating data. In the context of Open Education, the potentially massive student cohorts and the global audience represent a great opportunity for significant analyses and breakthroughs in the field of learning analytics. However, these potentially huge datasets require proper analysis techniques, and different algorithms, tools and approaches may perform better in this specific context. In this work, we compare different clustering algorithms using an educational dataset. We start by identifying the most relevant algorithms in Learning Analytics and benchmark them to determine, according to internal validation and stability measurements, which algorithms perform better. We analyzed seven algorithms, and determined that K-means and PAM were the best performers among partition algorithms, and DIANA was the best performer among hierarchical algorithms.
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