Improving the adaptability of multi-agent based E-learning systems

  1. PINTO-SANTOS, Francisco 1
  2. SÁNCHEZ SAN BLAS, Hector 1
  4. MAO, Xuzeng 1
  1. 1 Universidad de Salamanca

    Universidad de Salamanca

    Salamanca, España


ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Datum der Publikation: 2018

Ausgabe: 7

Nummer: 4

Seiten: 5-16

Art: Artikel

DOI: 10.14201/ADCAIJ20187516 DIALNET GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal


Zitate erhalten

  • Zitate in 'Web of Science': 0 (13-10-2023)
  • Zitate in Dimensions: 0 (15-01-2024)


  • Sozialwissenschaften: C

Journal Citation Indicator (JCI)

  • Jahr 2018
  • JCI der Zeitschrift: 0.12
  • Höchstes Quartil: Q4
  • Bereich: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartil: Q4 Position im Bereich: 166/171


(Aktualisierte Daten ab 15-01-2024)
  • Gesamtzitate: 0
  • Letzten Termine (2 jahre): 0
  • Field Citation Ratio (FCR): 0.0


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.

Bibliographische Referenzen

  • 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.