ADAM: Advisory agents modeling system to enhance student-supervisor decision making

Web rapid development has provided new learning environments, bringing online education as a necessity in many sectors of the society. In such an environment, selecting supervisor is a critical decision that graduate students as well as professors are involved with which could benefit from e-learning tools. In this paper we have proposed ADAM, an ADvisor Agent Modeling system, which is a solution for supervisor selection based on multi-agent. In order to simulate student-professor relation, we have profiled them through deriving their decision parameters and other required information, using data obtained from various sources including Learning Management System (LMS), Community of Practice (COP), as well as our question answering user interface. ADAM utilizes a weighted ontology, which is proposed by authors, to improve agent decision making based on their provided model. Proposed advisory system is implemented at the University of Tehran, using more than 50 different profiles of students and professors. ADAM can be embedded in any web based educational system and any group activities which need forming hierarchical relationships between different members who have their own demands and goals.