Case-based student modeling in multi-agent learning environment

The student modeling (SM) is a core component in the development of Intelligent Learning Environments (ILEs). In this paper we describe how a Multi-agent Intelligent Learning Environment can provide adaptive tutoring based in Case-Based Student Modeling (CBSM). We propose a SM structured as a multi-agent system composed by four types of agents. These are: the Case Learner Agent (CLA), Tutor Agent (TA), Adaptation Agent (AA), and Orientator Agent (OA). Each student model has a corresponding CLA. The TA Agent selects the adequate teaching strategy. The AA Agent organizes the learning resources and the OA Agent personalizes the learning considering the psychological characteristics of the student. To illustrate the process of student modeling an algorithm will also be presented. To validate the Student Model, we present a case study based an Intelligent Tutoring System for learning in Public Health domain.