Optimizing Online Educational Experiences through Semantic Ontology-Based Recommender Systems: A Case Study on Coursera
DOI:
https://doi.org/10.22399/ijcesen.3365Keywords:
Coursera, Web Scraping, Ontology Design, Content-Based Recommendation, PythonAbstract
With the rapid expansion of Massive Open Online Courses (MOOCs), learners face increasing challenges in identifying relevant educational content tailored to their interests and needs. This paper presents the design and implementation of a content-based recommendation system (CBRS) for Coursera courses, enhanced through the use of Semantic Web and ontology technologies. We constructed a domain-specific OWL ontology using real-world data extracted from over 1,000 Coursera courses, capturing key attributes. The system leverages semantic representations to improve the accuracy and relevance of recommendations by computing similarities between course contents and user preferences. The recommendation engine was evaluated using a test set of user queries and relevance judgments. Experimental results show strong performance, achieving Precision 0.98%, Recall 1.00, F1 Score ≈0.99% and Accuracy 0.98%. These findings demonstrate the system’s effectiveness in delivering personalized, high-quality recommendations and underscore the value of integrating ontologies into educational recommender systems. This work contributes to the advancement of intelligent e-learning systems by enhancing resource discoverability, user engagement, and overall learning experience in MOOC platforms.
References
[1] Jena, K. K., et al. (2023). E-learning course recommender system using collaborative filtering models. Electronics (Switzerland), 12(1). https://doi.org/10.3390/electronics12010157
[2] Ma, F. (2025). Learning behavior analysis and personalized recommendation system of online education platform based on machine learning. Computers and Education: Artificial Intelligence, 8. https://doi.org/10.1016/j.caeai.2025.100408
[3] Amanoul, S. V., Abdulrahman, L. M., Abdullah, R. M., & Qashi, R. (2023). Orchestrating distributed computing and web technology with semantic web and big data. Journal of Smart Internet of Things, 2023(2), 174–192. https://doi.org/10.2478/jsiot-2023-0019
[4] Gan, B., Zhang, C., Dong, Q., & Sun, W. (2021). Design of online course knowledge recommendation system based on improved learning diagnosis model. In Journal of Physics: Conference Series. IOP Publishing. https://doi.org/10.1088/1742-6596/1861/1/012052
[5] Abdulazeez, D., & Salah, R. (2020). Developing an ontology for retrieving massive open online courses (MOOCs) information in Coursera platform. The Journal of The University of Duhok, 23(1), 103–114. https://doi.org/10.26682/sjuod.2020.23.1.11
[6] Hazar, M. J., Zrigui, M., & Maraoui, M. (2022). Learner comments-based recommendation system. In Procedia Computer Science, 2000–2012. https://doi.org/10.1016/j.procs.2022.09.259
[7] Islam, N., Khan, A. R., Ahmed, U., Jaffer, A., & Akhtar, S. (2022). Course recommendation, exploratory data analysis and visualizations of massive open online courses (MOOCs). Journal of Independent Studies and Research: Computing, 20(1). https://doi.org/10.31645/JISRC.22.20.1.9
[8] Ben Hadj Boubaker, N., Kodia, Z., & Yacoubi Ayadi, N. (2024). Learning knowledge graph-based recommender system using ensemble attention networks. In 16th International Conference on Management of Digital Ecosystems. https://hal.science/hal-04892534v1
[9] Wei, X., Sun, S., Wu, D., & Zhou, L. (2021). Personalized online learning resource recommendation based on artificial intelligence and educational psychology. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.767837
[10] Amin, S., Uddin, M. I., Mashwani, W. K., Alarood, A. A., Alzahrani, A., & Alzahrani, A. O. (2023). Developing a personalized e-learning and MOOC recommender system in IoT-enabled smart education. IEEE Access, 11, 136437–136455. https://doi.org/10.1109/ACCESS.2023.3336676
[11] Ezaldeen, H., Bisoy, S. K., Misra, R., & Alatrash, R. (2023). Semantics aware intelligent framework for content-based e-learning recommendation. Natural Language Processing Journal, 3, 100008. https://doi.org/10.1016/j.nlp.2023.100008
[12] Putra, S. A., et al. (2024). The development of intelligent web for rural social e-learning. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 13(3), 112–121. https://doi.org/10.22146/jnteti.v13i3.9872
[13] Bousalem, S., Benchikha, F., & Marir, N. (2025). Personalized learning through MBTI prediction: A deep learning approach integrated with learner profile ontology. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3542701
[14] Ma, F. (2025). Learning behavior analysis and personalized recommendation system of online education platform based on machine learning. Computers and Education: Artificial Intelligence, 8. https://doi.org/10.1016/j.caeai.2025.100408
[15] Wang, L., Han, W., & Xi, Z. (2025). Exploring semantic web tools in education to boost learning and improve organizational efficiency. International Journal on Semantic Web and Information Systems, 21(1). https://doi.org/10.4018/IJSWIS.370315
[16] Hazar, M. J., Jaballi, S., Maraoui, M., Zrigui, M., & Nicolas, H. (2025). A hybrid e-learning recommendation system incorporating user reviews and ratings for enhanced course selection. https://doi.org/10.21203/rs.3.rs-5729775/v1
[17] Dutta, S., Beier, F., & Werth, D. (2025). AI-based personalized multilingual course recommender system using large language models. In International Conference on Agents and Artificial Intelligence (pp. 1069–1076). Science and Technology Publications. https://doi.org/10.5220/0013260100003890
[18] Cheok, S. M., Hoi, L. M., Tang, S. K., & Tse, R. (2021). Crawling parallel data for bilingual corpus using hybrid crawling architecture. In Procedia Computer Science (pp. 122–127). https://doi.org/10.1016/j.procs.2021.12.218
[19] Vo, N. N. Y., Vu, Q. T., Vu, N. H., Vu, T. A., Mach, B. D., & Xu, G. (2022). Domain-specific NLP system to support learning path and curriculum design at tech universities. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2021.100042
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