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2021 - Volume 4 - Number 1

Multi-Agent Adaptive e-Learning System Based on Learning Styles

Faith Ngami Kivuva * kivuva.faith@ku.ac.ke * ORCID: 0000-0002-0473-0073 * ResearcherID: AAU-5510-2021
Kenyatta University, School of Engineering and Technology, Nairobi, KENYA

Elizaphan Maina * maina.elizaphan@ku.ac.ke * ORCID: 0000-0003-1917-9760
Kenyatta University, School of Engineering and Technology, Nairobi, KENYA

Rhoda Gitonga * gitonga.rhoda@ku.ac.ke * ORCID: 0000-0002-9625-8086 * ResearcherID: AAU-6428-2021
Kenyatta University, Digital School, Nairobi, KENYA

Open Journal for Information Technology, 2021, 4(1), 1-12 * https://doi.org/10.32591/coas.ojit.0401.01001k
Received: 17 April 2021 ▪ Accepted: 18 July 2021 ▪ Published Online: 9 August 2021

LICENCE: Creative Commons Attribution 4.0 International License.

ARTICLE (Full Text - PDF)

ABSTRACT:
Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

KEY WORDS: personalized feedback, Moodle, intelligent agents, learning styles, recommendation.

CORRESPONDING AUTHOR:
Faith Ngami Kivuva, Kenyatta University, School of Engineering and Technology, Nairobi, KENYA. E-mail: kivuva.faith@ku.ac.ke.

REFERENCES:

Alexandru, A., Tîrziu, E., Tudora, E., & Bica, O. (2015). Enhanced education by using intelligent agents in multi-agent adaptive e-Learning systems. Studies in Informatics and Control, 24(1), 13-22. https://doi.org/10.24846/v24i1y201502

Alexandru, A., Tîrziu, E., Tudora, E., & Bica, O. (2016). Enhanced education by using intelligent agents in multi-agent adaptive e-Learning systems. Studies in Informatics and Control, 24(1), 13-22. https://doi.org/10.24846/v24i1y201502

Balasubramanian, V., & Margret Anouncia, S. (2018). Learning style detection based on cognitive skills to support adaptive learning environment – A reinforcement approach. Ain Shams Engineering Journal, 9(4), 895-907. https://doi.org/10.1016/j.asej.2016.04.012

Daoruang, B., Chaichomchuen, S., & Mingkhwan, A. (2018). An adaptive learning system based on proportional VARK to enhance learning achievement concept. The 4th Joint Symposium on Computational Intelligence (JSCI 4).

Diaz, F. S., Rubilar, T. P., Figueroa, C. C., & Silva, R. M. (2018). An adaptive E-learning platform with VARK learning styles to support the learning of object orientation. EDUNINE 2018 - 2nd IEEE World Engineering Education Conference: The Role of Professional Associations in Contemporaneous Engineer Careers, Proceedings. https://doi.org/10.1109/EDUNINE.2018.8450990

El Fazazi, H., Elgarej, M., Qbadou, M., & Mansouri, K. (2021). Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Engineering, Technology & Applied Science Research, 11(1), 6637-6644. https://doi.org/10.48084/etasr.3905

Fasihfar, Z., & Rokhsati, H. (2017). Study of application of intelligent agents in e-learning systems. Bull. La Société R. Des Sci. Liège, 86(February), 398-405.

Hosni, H., El-Dosuky, M., & Eisa, M. (2020). Virtual learning environment and learning styles. Journal of Theoretical and Applied Information Technology, 98(17), 3595-3606.

Imran Hussain. (2019). Pedagogical implications of VARK Model of Learning. Journal of Literature, Languages and Linguistics, 38(November), 33-37.

Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2019). Rule-based adaptive user interface for adaptive E-learning system. Education and Information Technologies, 24(1), 613-641. https://doi.org/10.1007/s10639-018-9788-1

Kumar, A., Singh, N., & Ahuja, N. J. (2017). Learner characteristics based learning style models classification and its implications on teaching. Int. J. Pure Appl. Math. (IJPAM), 118(20), 175-184.

Lakkah, S. El, Alimam, M. A., & Seghiouer, H. (2017). Adaptive e-learning system based on learning style and ant colony optimization. 2017 Intelligent Systems and Computer Vision, ISCV 2017. https://doi.org/10.1109/ISACV.2017.8054963

Leka, L., Kika, A., & Greca, S. (2016). Adaptivity in e-learning systems. RTA-CSIT, 135-139.

McLeod, S. (2017). Kolb’s learning styles and experiential learning cycle. Simply Psychology.

Nadrljanski, M., Vukić, D., & Nadrljanski, D. (2018). Multi-agent systems in e-learning. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 – Proceedings, October, 990-995. https://doi.org/10.23919/MIPRO.2018.8400181

Oskouei, R. J., Varzeghani, H. N., & Samadyar, Z. (2014). Intelligent agents: A comprehensive survey. International Journal of Electronics Communication and Computer Engineering, 5(August), 2278-4209.

Pandey, A. (2017). Benefits of personalisation of elearning. https://www.eidesign.net/top-8-benefits-personalisation-elearning/.

Wu, C. H., Chen, Y. S., & Chen, T. C. (2018). An Adaptive e-learning system for enhancing learning performance: Based on dynamic scaffolding theory. Eurasia Journal of Mathematics, Science and Technology Education, 14(3), 903-913. https://doi.org/10.12973/ejmste/81061