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Analyzing Student Behavior in Moodle System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 80 |
| Year of Publication: 2025 |
| Authors: Yassine Chajri, Mohammed Chajri |
10.5120/ijca2025924745
|
Yassine Chajri, Mohammed Chajri . Analyzing Student Behavior in Moodle System. International Journal of Computer Applications. 186, 80 ( Apr 2025), 54-59. DOI=10.5120/ijca2025924745
Abstract
Educational data mining is an interesting discipline that focuses on developing methods to extract knowledge and discover patterns from online learning systems. This work is an application of data mining in learning management systems. Our objective is to introduce educational data mining by describing a step-by-step process using a variety of techniques such as Attribute Weighting, Classification, Clustering, and Association Rules to achieve the goal of discovering useful knowledge from Moodle. For association rules, we will present a comparison between two data mining algorithms, Apriori and FP-Growth, to justify our choice of the FP-Growth algorithm. Analyzing mining results enables teachers to better allocate resources and understand student behavior.
References
- Djamel Abdelkader ZIGHED, Ricco RAKOTOMALALA: Extraction des Connaissances à partir des Données.
- Alejandro Pena-Ayala, Educational data mining: A surveyand a data mining-based analysis of recent works.
- Martin Dougiamas, How we built a community around open-source software.
- Brijesh Kumar Baradwaj, Saurabh Pal ; Mining Educational Data to Analyze Students Performance in International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011 .
- Baker, M.,(2010). Data Mining for Education. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevie.
- Fabricio Voznika, Leonar Doviana, Data mining Classification.
- Nicolas Baskiotis , Michèle Sebag, C4.5 Competence Map: a Phase Transition-inspired Approach.
- Jiawei Han, Micheline Kamber and Anthoney K .H.Tung, Spatial Clustering Methods In Data Mining : A Survey in School Computing Science, Simon Fraser University.
- Cristóbal Romero , Sebastián Ventura, Enrique García : Data mining in course management systems: Moodle case study and tutorial.
- Vance Faber : Clustering and the Continuous k-Means Algorithm.
- Sotiris Kotsiantis, Dimitris Kanellopoulos,Association Rules Mining: A Recent Overview ; GESTS International Transactions on Computer Science and Engineering, Vol.32 (1), 2006, pp. 71-82.
- Agrawal, R., Imielinski, T., and Swami, A. N. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.
- R. Agrawal, T. Imelinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Database,” Proc. ACM-SIGM OD International Conference, pp. 208-216, 1993.
- A. Salleb and C. Vrain, “An application of association rules discovery to geographic information systems,” Proc. The 4th European Conference on Principles of Data Mining and Knowledge Discovery PKDD, pp. 613-618, 2000.
- Y Jaya Babu, G J Phani Bala, Siva Rama Krishna T Extraction Spatial Association Rules From the Maximum Frequent Itemsets based on Boolean Matrix : International Journal of engineering Science & Advanced Technology Volume - 2, Issue - 1, 79 – 84.
- J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In: Proc. Conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, NY, USA 2000.
- Christian Borgelt, An Implementation of the Fpgrowth Algorithm in Department of Knowledge Processing and Language Engineering School of Computer Science, OttovonGuerickeUniversity of Magdeburg Universit atsplatz 2, 39106 Magdeburg, Germany.
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