Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining and Text Mining Technologies for Collaborative Learning in an ILMS "Samurai"
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
Data Mining Techniques in e-Learning CelGrid System
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
User Modeling and User-Adapted Interaction
Course Ranking and Automated Suggestions through Web Mining
ICALT '10 Proceedings of the 2010 10th IEEE International Conference on Advanced Learning Technologies
Homogeneity and Enrichment: Two Metrics for Web Applications Assessment
PCI '10 Proceedings of the 2010 14th Panhellenic Conference on Informatics
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Learning management systems (LMSs) are software tools designed to manage user learning interventions. On the other hand, in most LMSs there are neither appropriate metrics nor algorithms embedded in them which would facilitate their qualitative and quantitative measurement. The purpose of this paper is to use existing techniques in a different way, in order to analyse the log file of an LMS. Three metrics, enrichment, homogeneity and interest in course usage measurement are used. Data mining techniques, such as classification, clustering and association, are applied to the LMS data with the use of the open source software tool Weka. Two algorithms for course classification and suggestion actions are also described. A case study based on the proposed approach was applied to LMS data from a Greek University. The results confirmed the validity of the approach and showed a strong relationship between the course usage and the corresponding students' grades in the exams.