Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning
Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.
Keywords:adaptative e-learning system, knowledge level, learning path recommendation, learning styles, multi-agent, Q-learning, reinforcement learning, students’ disabilities
M. Laaziri, S. Khoulji, K. Benmoussa, and K. M. Larbi, "Outlining an Intelligent Tutoring System for a University Cooperation Information System," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3427-3431, Oct. 2018. https://doi.org/10.48084/etasr.2158
M. T. Alshammari and A. Qtaish, "Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level," Journal of Information Technology Education: Research, vol. 18, pp. 529-547, Nov. 2019. https://doi.org/10.28945/4459
M. Abdullah, W. H. Daffa, R. M. Bashmail, M. Alzahrani, and M. Sadik, "The Impact of Learning Styles on Learner's Performance in E-Learning Environment," International Journal of Advanced Computer Science and Applications, vol. 6, no. 9, 45/01 2015. https://doi.org/10.14569/IJACSA.2015.060903
R. Felder, "Learning and Teaching Styles in Engineering Education," Journal of Engineering Education -Washington-, vol. 78, no. 7, pp. 674-681, Jan. 1988.
M. U. Bokhari and S. Ahmad, "Multi-Agent Based E-Learning Systems: A Comparative Study," in Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, New York, NY, USA, Oct. 2014, pp. 1-6. https://doi.org/10.1145/2677855.2677875
P. Q. Dung and A. M. Florea, "An Architecture and a Domain Ontology for Personalized Multi-agent e-Learning Systems," in 2011 Third International Conference on Knowledge and Systems Engineering, Hanoi, Vietnam, Oct. 2011, pp. 181-185. https://doi.org/10.1109/KSE.2011.35
C. Giuffra and R. Silveria, "A multi-agent system model to integrate Virtual Learning Environments and Intelligent Tutoring Systems," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 2, Special Issue on Artificial Intelligence and Social Application, 2013. https://doi.org/10.9781/ijimai.2013.217
M. Krendzelak, "Machine learning and its applications in e-learning systems," in 2014 IEEE 12th IEEE International Conference on Emerging eLearning Technologies and Applications (ICETA), Stary Smokovec, Slovakia, Dec. 2014, pp. 267-269. https://doi.org/10.1109/ICETA.2014.7107596
M. Boussakssou, B. Hssina, and M. Erittali, "Towards an Adaptive E-learning System Based on Q-Learning Algorithm," Procedia Computer Science, vol. 170, pp. 1198-1203, Jan. 2020. https://doi.org/10.1016/j.procs.2020.03.028
W. Intayoad, C. Kamyod, and P. Temdee, "Reinforcement Learning for Online Learning Recommendation System," in 2018 Global Wireless Summit (GWS), Chiang Rai, Thailand, Nov. 2018, pp. 167-170. https://doi.org/10.1109/GWS.2018.8686513
K. R. Premlatha and T. V. Geetha, "Learning content design and learner adaptation for adaptive e-learning environment: a survey," Artificial Intelligence Review, vol. 44, no. 4, pp. 443-465, Dec. 2015. https://doi.org/10.1007/s10462-015-9432-z
"Index of Learning Styles Questionnaire," NC State University. https://www.webtools.ncsu.edu/learningstyles/ (accessed Dec. 20, 2020).
N. E. A. Amrani, O. E. K. Abra, M. Youssfi, and O. Bouattane, "A Novel Deep Learning Approach for Semantic Interoperability Between Heteregeneous Multi-Agent Systems," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4566-4573, Aug. 2019. https://doi.org/10.48084/etasr.2841
How to Cite
MetricsAbstract Views: 716
PDF Downloads: 505
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the 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) after its publication in ETASR with an acknowledgement of its initial publication in this journal.