Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

Meryem Amane, Karima Aissaoui, Mohammed Berrada


The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results.


Doi: 10.28991/ESJ-2023-07-03-010

Full Text: PDF


Learning Objects; Multi-Label Classification (MLC); Web-Based Mining Techniques; Fuzzy C-Means Clustering Algorithm; Machine Learning.


Churchill, D. (2007). Towards a useful classification of learning objects. Educational Technology Research and Development, 55(5), 479–497. doi:10.1007/s11423-006-9000-y.

Amane, M., Aissaoui, K., & Berrada, M. (2022). Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection. International Journal of Emerging Technologies in Learning, 17(20), 248–260. doi:10.3991/ijet.v17i20.32323.

Sicilia, M. A., & García, E. (2003). On the concepts of usability and reusability of learning objects. International Review of Research in Open and Distance Learning, 4(2), 26–38. doi:10.19173/irrodl.v4i2.155.

López, V. F., De La Prieta, F., Ogihara, M., & Wong, D. D. (2012). A model for multi-label classification and ranking of learning objects. Expert Systems with Applications, 39(10), 8878–8884. doi:10.1016/j.eswa.2012.02.021.

Omran, M. G. H., Engelbrecht, A. P., & Salman, A. (2007). An overview of clustering methods. Intelligent Data Analysis, 11(6), 583–605. doi:10.3233/ida-2007-11602.

Yasunori, E., Yukihiro, H., Makito, Y., & Sadaaki, M. (2009). On semi-supervised fuzzy c-means clustering. 2009 IEEE International Conference on Fuzzy Systems. doi:10.1109/fuzzy.2009.5277177.

Lampezhev, A. H., Linskaya, E. Y. E., Tatarkanov, A. A. B., & Alexandrov, I. A. (2021). Cluster data analysis with a fuzzy equivalence relation to substantiate a medical diagnosis. Emerging Science Journal, 5(5), 688–699. doi:10.28991/esj-2021-01305.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–53. doi:10.1609/aimag.v17i3.1230.

Zschech, P., Horn, R., Höschele, D., Janiesch, C., & Heinrich, K. (2020). Intelligent User Assistance for Automated Data Mining Method Selection. Business and Information Systems Engineering, 62(3), 227–247. doi:10.1007/s12599-020-00642-3.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. doi:10.1007/s12525-021-00475-2.

Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE). doi:10.1109/ictke.2017.8259629.

Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576–597. doi:10.28991/esj-2021-01298.

Becht, E., McInnes, L., Healy, J., Dutertre, C. A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology, 37(1), 38–47. doi:10.1038/nbt.4314.

Devi, R. G., & Sumanjani, P. (2015). Improved classification techniques by combining KNN and Random Forest with Naive Bayesian classifier. 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India. doi:10.1109/icetech.2015.7274997.

Chaker, J., & Khaldi, M. (2019). A New Metadata Scheme for Multimedia and Intelligent Learning Objects. International Journal of Engineering and Advanced Technology, 9(2), 3340–3345. doi:10.35940/ijeat.b3746.129219.

Verbert, K., & Duval, E. (2008). ALOCOM: A generic content model for learning objects. International Journal on Digital Libraries, 9(1), 41–63. doi:10.1007/s00799-008-0039-8.

Dodani, M. H. (2002). The dark side of object learning: Learning objects. Journal of Object Technology, 1(5), 37–42. doi:10.5381/jot.2002.1.5.c3.

Klašnja-Milićević, A., Ivanović, M., Vesin, B., & Budimac, Z. (2018). Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence, 48(6), 1519–1535. doi:10.1007/s10489-017-1051-8.

Joy, J., & Pillai, R. V. G. (2022). Review and classification of content recommenders in E-learning environment. Journal of King Saud University - Computer and Information Sciences, 34(9), 7670–7685. doi:10.1016/j.jksuci.2021.06.009.

Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9), 552. doi:10.3390/educsci11090552.

Anantharaman, H., Mubarak, A., & Shobana, B. T. (2018). Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification. 2018 IEEE Conference on E-Learning, e-Management and e-Services (IC3e), Langkawi, Malaysia. doi:10.1109/ic3e.2018.8632646.

Aldrees, A., & Chikh, A. (2016). Comparative evaluation of four multi-label classification algorithms in classifying learning objects. Computer Applications in Engineering Education, 24(4), 651–660. doi:10.1002/cae.21743.

Carrillo, D., López, V.F., Moreno, M.N. (2013). Multi-label Classification for Recommender Systems. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent Systems and Computing, 221, Springer, Cham, Switzerland. doi:10.1007/978-3-319-00563-8_22.

González, P., Gibaja, E., Zapata, A., Menéndez, V. H., & Romero, C. (2017). Towards automatic classification of learning objects: Reducing the number of used features. Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, 25-28 June, 2017, Wuhan, China.

Batista, V.F.L., Pintado, F.P., Gil, A.B., Rodríguez, S., Moreno, M.N. (2011). A System for Multi-label Classification of Learning Objects. Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, 87, Springer, Berlin, Germany. doi:10.1007/978-3-642-19644-7_55.

Birzniece, I., Rudzajs, P., Kalibatiene, D. (2014). Evaluating the Application of Interactive Classification System in University Study Course Comparison. Perspectives in Business Informatics Research. BIR 2014. Lecture Notes in Business Information Processing, 194, Springer, Cham, Switzerland. doi:10.1007/978-3-319-11370-8_24.

Rani, M., & Kumar, V. (2020). Multi-label classification of learning objects using machine learning algorithms. International Journal of Intelligent Systems and Applications, 12, 36–43.

Gouiouez, M. (2021). A Fuzzy Near Neighbors Approach for Arabic Text Categorization Based on Web Mining Technique. Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, 211, Springer, Cham, Switzerland. doi:10.1007/978-3-030-73882-2_52.

Chung, C., Babin, L.A. (2017). New Technology for Education: Moodle. The Customer is NOT Always Right? Marketing Orientationsin a Dynamic Business World. Developments in Marketing Science: Proceedings of the Academy of Marketing Science, Springer, Cham, Switzerland. doi:10.1007/978-3-319-50008-9_182.

Bartuskova, A., Krejcar, O., Soukal, I. (2015). Framework of Design Requirements for E-learning Applied on Blackboard Learning System. Computational Collective Intelligence. Lecture Notes in Computer Science, 9330, Springer, Cham, Switzerland. doi:10.1007/978-3-319-24306-1_46.

López-Belmonte, J., Pozo-Sánchez, S., Carmona-Serrano, N., & Moreno-Guerrero, A.-J. (2022). Flipped Learning and E-Learning as Training Models Focused on the Metaverse. Emerging Science Journal, 6, 188–198. doi:10.28991/esj-2022-sied-013.

Full Text: PDF

DOI: 10.28991/ESJ-2023-07-03-010


  • There are currently no refbacks.

Copyright (c) 2023 Meryem Amane, KARIMA AISSAOUI, mohammed berrada