Multi-agent system-based framework for an intelligent management of competency building
Document Type
Article
Source of Publication
Smart Learning Environments
Publication Date
9-27-2024
Abstract
To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, therefore, needed to intelligently coordinate all the learning activities while self-adapting to the ongoing progress of learning as well as to the specific requirements and backgrounds of learners. To address these issues, we propose in this paper a new framework for an intelligent management of the competency building process during learning. Our framework is based on a recursive spiral Assess-Predict-Oversee-Transit model that is orchestrated by a multi-agent system. This system is particularly responsible of enabling smart transitions between learning stages. It is also responsible of assessing and predicting the process of competency building of the learner and, then, making the right decisions about the learning progress, accordingly. Results of our solution were demonstrated via an Augmented Reality app that we created using the Unity3D engine to train learners on Air Conditioner maintenance.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Volume
11
Issue
1
First Page
41
Last Page
41
Disciplines
Computer Sciences | Education
Keywords
Competency building, Stage of learning, Learning index, Competency index, Key performance indicator, Multi-agent system, Augmented reality
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Outay, Fatma; Jabeur, Nafaa; Bellalouna, Fahmi; and Al Hamzi, Tasnim, "Multi-agent system-based framework for an intelligent management of competency building" (2024). All Works. 6828.
https://zuscholars.zu.ac.ae/works/6828
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series