Design and Analysis of a Multi-Agent E-Learning System Using Prometheus Design Tool
This work addresses the problem of assessing prior learning skills in students for personalized recommendations, but it appears incremental as it applies an existing modeling tool to a specific e-learning domain.
The paper tackles the design and analysis of a multi-agent e-learning system for pre-assessment of student skills, using the Prometheus AUML approach to model five interactive agents, and presents data analysis and prediction models for future results.
Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.