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.

[1]  Luc Lamontagne,et al.  Case-Based Reasoning Research and Development , 1997, Lecture Notes in Computer Science.

[2]  A. Joussellin,et al.  A link between k-nearest neighbor rules and knowledge based systems by sequence analysis , 1987, Pattern Recognit. Lett..

[3]  Victor Maojo,et al.  Biological and Medical Data Analysis, 6th International Symposium, ISBMDA 2005, Aveiro, Portugal, November 10-11, 2005, Proceedings , 2005, ISBMDA.

[4]  Kinshuk,et al.  Student Model for Distance Education System in Maldives , 2003 .

[5]  Maria Virvou,et al.  Authoring intelligent tutoring systems over the World Wide Web , 2002, Proceedings First International IEEE Symposium Intelligent Systems.

[6]  Thad Crews Intelligent learning environments: using educational technology to assist complex problem solving , 1997, Proceedings Frontiers in Education 1997 27th Annual Conference. Teaching and Learning in an Era of Change.

[7]  Mathias Bauer A dempster-shafer approach to modeling agent preferences for plan recognition , 2005, User Modeling and User-Adapted Interaction.

[8]  J. Self Grounded in reality: the infiltration of AI into practical educational systems , 1998 .

[9]  Ramzan Khuwaja,et al.  Architecture of CIRCSIM-Tutor (v.3): a smart cardiovascular physiology tutor , 1994, Proceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS).

[10]  Sharon J. Derry,et al.  Individualized Tutoring Using an Intelligent Fuzzy Temporal Relational Database , 1990, Int. J. Man Mach. Stud..

[11]  Vladan Devedzic,et al.  Design pattern ITS: student model implementation , 2004, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings..

[12]  J. Alfredo Sánchez,et al.  Ontological agents model based on MAS-CommonKADS methodology , 2004, 14th International Conference on Electronics, Communications and Computers, 2004. CONIELECOMP 2004..

[13]  Huajun Chen,et al.  On case-based knowledge sharing in semantic Web , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[14]  P. D. O'Brien,et al.  FIPA — Towards a Standard for Software Agents , 1998 .

[15]  Susan Craw,et al.  Learning and Applying Case-Based Adaptation Knowledge , 2001, ICCBR.

[16]  Ashok Patel,et al.  A Plug-able Web-based Intelligent Tutoring System , 2002, ECIS.

[17]  Juan C. Burguillo,et al.  SINCO: Intelligent System in Disease Prevention and Control. An Architectural Approach , 2004, ISBMDA.

[18]  Helmut Simm,et al.  A cognitive load reduction approach to exploratory learning and its application to an interactive si , 2000 .

[19]  Martha C. Polson,et al.  Foundations of intelligent tutoring systems , 1988 .

[20]  Eva L. Ragnemalm Student diagnosis in practice; bridging a gap , 2005, User Modeling and User-Adapted Interaction.

[21]  Masoud Yazdani,et al.  Intelligent tutoring systems: An overview , 1986 .