Multi-agent system-based framework for an intelligent management of competency building

• Published in: Smart learning environments Vol. 11; no. 1; pp. 41 - 18
• Main Authors: Outay, Fatma, Jabeur, Nafaa, Bellalouna, Fahmi, Al Hamzi, Tasnim
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Air conditioners; Augmented reality; Climate Control; Competence; Competency building; Competency index; Computers and Education; Education; Instructional Effectiveness; Key performance indicator; Learning; Learning activities; Learning index; Multi-agent system; Multiagent systems; Predictive maintenance; Recursive functions; Stage of learning; Competency building; Multi-agent system; Stage of learning; Key performance indicator; Competency index; Learning index; Augmented reality
• ISSN: 2196-7091; 2196-7091
• DOI: 10.1186/s40561-024-00328-3

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.