Journals / Turkish Online Journal of Distance Education / 2019 / Cilt: 20 - Sayı: 1

Dynamic Multi Agent System for Revising E-Learning Content Materia

Pages
131–144
Publication Date
2019-01-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As result, the tests and experimentations done in this context have proved the effectiveness of the proposed solution, and revealed positive results both in term of learning process and learners’ feedback.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The growth of the information and communication technologies has led to the appearance of new concepts, approach and disciplines.
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Key Findings & Data:For learners, an e-learning system constitutes rich window to the knowledge.
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Conclusion & Impact:As result, the tests and experimentations done in this context have proved the effectiveness of the proposed solution, and revealed positive results both in term of learning process and learners’ feedback.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Learning · updates · revising

Abstract

The growth of the information and communication technologies has led to the appearance of new concepts, approach and disciplines. For learners, an e-learning system constitutes rich window to the knowledge. It presents a varied training, including different content material format (video, text, interactive content...) and diverse methods. In order to keep learners’ attention, e-learning system must provide good content's quality, including revised material and updated methods. In this perspective, we have implemented multi-agent system composed of three sort of agents ensuring a permanent revision to the e-learning content. The first one is called Checker Agent (CA). It checks the educational resources, and detects the outdated ones so as to be treated. The second agent is named Search Agent (SA). The task of this one is to look for recent contents and new teaching methods. Whereas, the third agent is called Updater Agent (UA). Its function consists on inserting the retrieved updates corresponding to each content. The communication between these agents is ensured by an XML files. In this paper, we have proposed an implementation of the first part of our system. Namely, the checking process of e-learning curriculum by implementing the CA algorithm. And the integration process by implementing the UA algorithm. As result, the tests and experimentations done in this context have proved the effectiveness of the proposed solution, and revealed positive results both in term of learning process and learners’ feedback.

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Cite This Article

ELGHIBARI, F., ELOUAHBI, R., KHOUKHI, F. E. (2019). Dynamic Multi Agent System for Revising E-Learning Content Materia. Turkish Online Journal of Distance Education, Cilt: 20 - Sayı: 1, 131–144. https://ajindex.com/articles/140068

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References (10)

  1. Anghour, A., & Lamolle, M. (2017). Designing and Managing a Real-Time Collaborative Learning Paths by a Multi-agents Platform. Springer: Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, 571. Méndez, N. D., Marín, P. A., & Carranza, D. A. (2018). Intelligent Personal Assistant for Educational Material Recommendation Based on CBR. Springer:Personal Assistants: Emerging Computational Technologies. Intelligent Systems Reference Library, 132, 113-131.
  2. Song, D., Oh, E. Y., & Rice, M. (2017). Interacting with a conversational agent system for educational purposes in online courses. 10th International Conference on Human System Interactions (HSI). Ulsan, South Korea: the IEEE Digital Library Explorer. Yılmaz, F. G., Olpak, Y. Z., & Yılmaz, R. (2017). The Effect of the Metacognitive Support via Pedagogical Agent on Self-Regulation Skills. Journal of Educational Computing Research, 56(2), 159-180.
  3. Agarwal, N., & Ahmed, F. (2017, January). Developing collective learning extension for rapidly evolving information system courses. Springer:Education and Information Technologies, 22(1), 7–37.
  4. Arif, M., Illahi, M., Karim, A., Shamshirband, S., Alam, K. A., Farid, S.,. Balas, V. E. (2014). An architecture of agent-based multi-layer interactive e-learning and e-testing platform. Springer Science+Business Media, 1-24.
  5. Arsham, H. (2014, March). Interactive Education: Impact of the Internet on Learning & Teaching. Retrieved Febraury 2018, from http://home.ubalt.edu: http://home.ubalt.edu/ntsbarsh/interactive.htm
  6. Bhaskaran, S., & Swaminathan, P. (2014). Intelligent Adaptive E-learning Model for Learning Management System. Research Journal of Applied Sciences, Engineering and Technology, 16(7), 3298-3303.
  7. Bourbia, R., Seridi, A., Hadjeris, M., & Seridi, H. (2012). An Adaptive Learning Based on Ant Colony and Collaborative Filtering. Proceedings of the World Congress on Engineering, 2, 1-3.
  8. Carliner, S., & Shank, P. (2008). The e-Learning Handbook: Past Promises, Present Challenges. San Francisco: Wiley. Chehbi, S., Elouahbi, R., & El khoukhi, F. (2016). Tracking of learners' motivation and involvement in moodle. The International Journal of Multi-disciplinary Sciences, 2(2), 22-28.
  9. Cojocariiu, A., & Stanciiu, C. O. (2009). XML Technologies in Computer Assisted Learning and Testing Systems. Annals. Computer Science Series, 7(1), 89-94.
  10. Daomin, X. (2013). Multi-Agent Based e-Learning Intelligent