|
Title:
|
COMPARATIVE ANALYSIS OF CLUSTERING ALGORITHMS WITHIN A WEB-BASED LMS |
|
Author(s):
|
Kyle E. De Freitas, Margaret Bernard |
|
ISBN:
|
978-989-8533-39-5 |
|
Editors:
|
Ajith P. Abraham, Antonio Palma dos Reis and Jörg Roth |
|
Year:
|
2015 |
|
Edition:
|
Single |
|
Keywords:
|
Clustering, Educational Data Mining, Learning Management Systems, Web Usage Mining, Moodle |
|
Type:
|
Full Paper |
|
First Page:
|
11 |
|
Last Page:
|
18 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Clustering analysis provides a useful way to group objects without having previous knowledge about the data being analysed. However, the choice of clustering techniques is dependent on the structure of the dataset analysed. While research has been conducted on the general performance of clustering algorithms using arbitrary and standard datasets, in this paper, we present a case-based experiment to show the relative performance of clustering algorithms with Learning Management System log data. We compare partition-based (K-Means), density-based (DBSCAN) and hierarchical (BIRCH) methods to determine which technique is the most appropriate for performing clustering analysis within the LMS. We conclude by showing that partition-based methods produce the highest silhouette coefficient values when analysing data generated within the LMS. |
|
|
|
|
|
|
