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Francisco Pinto-Santos
University of Salamanca
Hector Sánchez San Blas
University of Salamanca
Manuel Salgado De La Iglesia
University of Salamanca
Xuzeng Mao
University of Salamanca
Vol. 7 No. 4 (2018), Articles, pages 5-16
Accepted: May 6, 2019


E-Learning is a new learning approach that involves the use of electronic technologies to access education outside of a conventional classroom (Alonso Rincon,). The objective of E-Learning systems is to increase the students’ learning skills by providing a customized experience to each system user (Rodrigues, 2013). However, to accomplish this, it is necessary to monitor the continuous changes in the environment, mainly the students’ knowledge and skill acquisition. A multi-agent system architecture and a clustering algorithm are proposed for this purpose (as presented in (Rodrigues, 2014) This paper is an extension to the work of (Al-Tarabily, 2018) because it not only monitors changes in the student environment but also in the project environment, increasing the system’s adaptability and accuracy.


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Al-Tarabily, A. A. M. I., F. Abdel-Kader, 2018. Optimizing Dynamic Multi-Agent Performance in E-Learning Environment. -

Alonso Rincon, G. P. C. R., Prieto Tejedor. Collaborative learning via social computing. Frontiers of Information Technology Electronic Engineering.

Brusilovsky, P., 2003. Adaptive and intelligent technologies for Web-based education. Volume 13, pages 159-172.

Chen, C. S., Chen, 2016. Ontology-based adaptive dynamic e-learning map planning method for conceptual knowledge learning. Volume 11, pages 1-20. -

Chen, H., Hsieh, 2007. Mining learner profile utilizing association rule for Web-based learning diagnosis. Volume 33, pages 6-22. -

Cheng, H., Lin, 2009. Dynamic question generation system for Web-based testing using particle swarm optimization. Volume 36, pages 616-624. -

Daradoumis, X. C., Bassi, 2013. A review on massive e-learning (MOOC) design, delivery and assessment. De-Marcos, M. G., Pages, 2007. Competencybased learning object sequencing using particle swarms. Volume 2, pages 111-116.

Ester, S. X., Kriegel, 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.

GopalaKrishnan, S., 2016. A hybrid PSO with Naïve Bayes classifier for disengagement detection in online learning. Volume 50, pages 215-224. -

Hammouda, K., 2000. A Comparative Study of Data Clustering Techniques. page 1.

Hatamlou, 2013. Black hole: A new heuristic optimization approach for data clustering. Volume 222, pages 175-184. -

HOU, C. F. H., ZHOU, 2000. Approaches for Scaling DBSCAN Algorithm to Large Spatial Databases. Volume 15. -

Huang, H. J. K., Chen, 2008. Standardized course generation process using dynamic fuzzy Petri nets. Volume 34, pages 72-86. -

Kahiigi Kigozi, H. T., Ekenberg, 2008. Exploring the e-Learning State of Art. Volume 6, pages 77-88. Kamdar, K., Paliwal, 2018. A State of Art Review on Various Aspects of Multi-Agent System. Volume 27, page 15.

Kennedy, E., 1995. Particle Swarm Optimization.

Rivas, R., Chamoso, 2017. An Agent-Based Internet of Things Platform for Distributed Real Time Machine Control. Pages 1-5. -

Rodrigues, F. R., Gonçalves, 2013. E-Learning platforms and E-learning students: Building the bridge to success. Volume 1, pages 21-34.

Rodrigues, F. R., Gonçalves, 2014. Developing multimodal conversational agents for an enhanced e-learning experience. Volume 3, pages 13-26. -

Romero, V., 2007. Educational data mining: A survey from 1995 to 2005. Volume 33, pages 135-146. Solanki, S., Khushalani, 2007. A multi-agent solution to distribution systems restoration. Volume 22, pages 1026-1034. -

Soller, J. M., Martinez, 2005. From Mirroring to Guiding: Review of State of the Art Technology for Supporting Collaborative Learning. Volume IJAIED, pages 261-290.

Stathacopoulou, S. M., Grigoriadou, 2007. Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model. Volume 32, pages 955-975. -

Tran, D., Drab, 2013. Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Volume 120, pages 92-96. -

Ullmann, C. C. d. A., Ferreira, 2015. Formation of learning groups in cMoocs using particle swarm optimization. pages 3296-3304. -

Vazquez, G.-A. M., Ramirez, 2011. Designing adaptive learning itineraries using features modelling and swarm intelligence. Volume 20, pages 623-639. -

Wooldridge, 2009. Introduction to Multi-Agent Systems.