Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan 30:2022:2941840.
doi: 10.1155/2022/2941840. eCollection 2022.

Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Shrouk H Hessen  1   2 , Hatem M Abdul-Kader  1 , Ayman E Khedr  3 , Rashed K Salem  1
Affiliations

Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Shrouk H Hessen et al. Comput Intell Neurosci. .

Abstract

Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.

PubMed Disclaimer

Conflict of interest statement

All authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The main steps of the proposed system.
Figure 2
Figure 2
All features rank by applying the Extra Trees feature selection method.
Figure 3
Figure 3
The best algorithms for 13 features.
Figure 4
Figure 4
The best algorithms for 6 features.
See this image and copyright information in PMC

References

    1. Tarik A., Aissa H., Yousef F. Artificial intelligence and machine learning to predict student performance during the covid-19. Procedia Computer Science . 2021;184:835–840. doi: 10.1016/j.procs.2021.03.104. - DOI - PMC - PubMed
    1. Pineda-Jaramillo J. D. A review of Machine Learning (ML) algorithms used for modeling travel mode choice. Dyna . 2019;86(211):32–41. doi: 10.15446/dyna.v86n211.79743. - DOI
    1. Jin W. Research on machine learning and its algorithms and development. Journal of Physics: Conference Series . 2020;1544(1) doi: 10.1088/1742-6596/1544/1/012003.012003 - DOI
    1. Gao Z., Wu B. Research on the innovation system of university production and education integration based on computer big data. IOP Conference Series: Earth and Environmental Science . 2021;692(2) doi: 10.1088/1755-1315/692/2/022025.022025 - DOI
    1. Giuffra C., Silveria R., Silveira R. A. A multi-agent system model to integrate virtual learning environments and intelligent tutoring systems. International Journal of Interactive Multimedia and Artificial Intelligence . 2013;2(1):p. 51. doi: 10.9781/ijimai.2013.217. - DOI

LinkOut - more resources