TY - JOUR
KW - Clustering
KW - Computer Languages
KW - Data Analysis
KW - Engineering Students
KW - Performance Evaluation
KW - Unsupervised Learning
AU - Álvaro Martínez Navarro
AU - Pablo Moreno-Ger
AB - 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.
IS - Special Issue on Big Data and Open Education
M1 - 2
N2 - 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.
PY - 2018
SP - 9
EP - 16
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets
UR - http://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_2_1_pdf_84728.pdf
VL - 5
SN - 1989-1660
ER -