Intelligent Support System for Personalized Online Learning
Keywords:Personalised eLearning, pandemic, learning paths, recommender system
The current conditions of the COVID-19 pandemic have required universities to transfer educational processes in the online environment. eLearning systems provide educational institutions and students with the opportunity to effectively organize the educational process and share knowledge. They provide each student with freedom of access to information and flexibility of the learning process. The student can individually determine the duration and sequence of courses by changing the trajectory of the educational process following their needs. In the context of the pandemic, students and teachers have to optimize their work over the Internet. This requires more extended personalization of the learning process. Intelligent technologies allow you to construct personalized learning paths for each student, varying methods, forms, and speed of learning. This study presents the architecture of the e-learning support system for the selection of online resources and for including them in the student's learning path. The system developed as a set of personal agents and services that interact based on a set of interconnected ontological models. Ontologies provide a more adequate representation of online resources and compatibility of the user request format with descriptions of training resources from different developers. The system recommends training modules based on current requests and user characteristics that match their profile. The system dynamically updates the knowledge base user characteristics, thereby increasing the effectiveness of recommendations.
Alshalabi, I. A., Hamada, S., & Elleithy, K. (2015). Automated adaptive learning using smart shortest path algorithm for course units. 2015 Long Island Systems, Applications and Technology, Farmingdale, NY, 2015 (pp. 1-5). https://doi.org/10.1109/LISAT.2015.7160187
Biletskiy, Y., Baghi, H., Keleberda, I., & Fleming, M. (2009). An adjustable personalization of search and delivery of learning objects to learners. Expert Systems with Applications, 36(5), 9113–9120. https://doi.org/10.1016/j.eswa.2008.12.038
Chen, W., Niu, Z., Zhao, X., & Li, Y. (2012). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 17(2), 271–284. https://doi.org/10.1007/s11280-012-0187-z
Chrysoulas, C., & Fasli, M. (2017). Building an Adaptive E-Learning System. In Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU, 375-382, 2017, Porto, Portugal. https://doi.org/10.5220/0006326103750382
Chu, C.-P., Chang, Y.-C., & Tsai, C.-C. (2009). PC2PSO: personalized e-course composition based on Particle Swarm Optimization. Applied Intelligence, 34(1), 141–154. https://doi.org/10.1007/s10489-009-0186-7
Curlango-Rosas, C., Ponce, G. A., & Lopez-Morteo, G. A. (2011). A Specialized Search Assistant for Learning Objects. ACM Transactions on the Web, 5(4), 1–29. https://doi.org/10.1145/2019643.2019648
Dascalu, M.-I., Bodea, C.-N., Mihailescu, M. N., Tanase, E. A., & Pablos, P. O. D. (2016). Educational recommender systems and their application in lifelong learning. Behaviour & Information Technology, 35(4), 290–297. https://doi.org/10.1080/0144929x.2015.1128977
Dharshini, A. P., Chandrakumarmangalam, S., & Arthi, G. (2015). Ant colony optimization for competency based learning objects sequencing in e-learning. Applied Mathematics and Computation, 263, 332–341. https://doi.org/10.1016/j.amc.2015.04.067
Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-1-4899-7637-6_12
George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642. https://doi.org/10.1016/j.compedu.2019.103642
Gulzar, Z., Raj, L. A., & Leema, A. A. (2019). Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach. International Journal of Information and Communication Technology Education, 15(2), 85–107. https://doi.org/10.4018/ijicte.2019040106
Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1–11. https://doi.org/10.1016/j.compedu.2013.01.015
Lin, H.-F. (2010). An application of fuzzy AHP for evaluating course website quality. Computers & Education, 54(4), 877–888. https://doi.org/10.1016/j.compedu.2009.09.017
Odonnell, E., Lawless, S., Sharp, M., & Wade, V. P. (2015). A Review of Personalised E-Learning. International Journal of Distance Education Technologies, 13(1), 22–47. https://doi.org/10.4018/ijdet.2015010102
Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using institutional data to predict student course selections in higher education. The Internet and Higher Education, 29, 49–62. https://doi.org/10.1016/j.iheduc.2015.12.002
Peylo, C. (2000). W2 - Adaptive and Intelligent Web-Based Education Systems. In G. Gauthier, C. Frasson, K. VanLehn (Eds.), Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, 1839. Springer. https://doi.org/10.1007/3-540-45108-0_86
Sharma, M., & Ahuja, L. (2016). A Novel and Integrated Semantic Recommendation System for E-Learning using Ontology. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies - ICTCS 16, Article 52, 1-5. https://doi.org/10.1145/2905055.2905110
Sobecki, J., & Tomczak, J. M. (2010). Student Courses Recommendation Using Ant Colony Optimization. In N.T. Nguyen, M.T. Le, J. Świątek (Eds.), Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science, 5991. Springer. https://doi.org/10.1007/978-3-642-12101-2_14
Tarus, J. K., Niu, Z., & Mustafa, G. (2017). Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21–48. https://doi.org/10.1007/s10462-017-9539-5
Yang, F., Li, F. W. B., & Lau, R. W. H. (2010). An Open Model for Learning Path Construction. In X. Luo, M. Spaniol, L. Wang, Q. Li, W. Nejdl, W. Zhang (Eds.), Advances in Web-Based Learning – ICWL 2010. ICWL 2010. Lecture Notes in Computer Science, 6483. Springer. https://doi.org/10.1007/978-3-642-17407-0_33
How to Cite
Copyright (c) 2020 The Authors & LUMEN Publishing House
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant this journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work, with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as an earlier and greater citation of published work (See The Effect of Open Access).
BRAIN. Broad Research in Artificial Intelligence and Neuroscience Journal has an Attribution-NonCommercial-NoDerivs