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

Year of publication: 2018

Volume: 7

Issue: 4

Pages: 5-16

Type: Article

DOI: 10.14201/ADCAIJ20187516 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal


Cited by

  • Web of Science Cited by: 0 (13-10-2023)
  • Dimensions Cited by: 0 (15-01-2024)


  • Social Sciences: C

Journal Citation Indicator (JCI)

  • Year 2018
  • Journal Citation Indicator (JCI): 0.12
  • Best Quartile: Q4
  • Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartile: Q4 Rank in area: 166/171


(Data updated as of 15-01-2024)
  • Total citations: 0
  • Recent citations (2 years): 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.

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