01740nas a2200241   4500000000100000000000100001008004100002260001200043653001500055653002300070653001800093653002500111653002700136653002600163100003000189700002100219245008900240856009500329300000900424490000600433520104500439022001401484       2018                            d  c09/201810aClustering10aComputer Languages10aData Analysis10aEngineering Students10aPerformance Evaluation10aUnsupervised Learning1 aÁlvaro Martínez Navarro1 aPablo Moreno-Ger00aComparison of Clustering Algorithms for Learning Analytics with Educational Datasets  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_2_1_pdf_84728.pdf  a9-160 v53 aLearning 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.  a1989-1660