Carolina González

A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems

Intelligent tutoring systems (ITS) are interactive learning environments based on instruction assisted by computers. The intelligence of these systems is largely attributed to their ability to adapt to a specific student during the teaching process. In general, the adaptation process can be described by three phases: (i) getting the information about the student, (ii) processing the information to initialize and update a student model, and (iii) using the student model to provide the adaptation. In this paper we studied aspects related with student modeling (SM) in intelligent tutoring systems. First we make a qualitative comparison of two techniques: Bayesian networks (BN) and case-based reasoning (CBR) for SM. We apply both techniques to a case study concerning the development of an ITS for e-learning in the medical domain. Finally, we discuss the results obtained

Integrating Intelligent Tutoring Systems and Health Information Systems

The importance of integrating information about patients in intelligent tutoring systems (ITS) is the opportunity given to the students to direct their own learning. We propose an integration-architecture for sharing patient clinical data among tutoring and health information systems (HISs). HL 7 is used as a standard messaging protocol for collecting the clinical patient data distributed over health information systems.

SINCO: Intelligent System in Disease Prevention and Control. An Architectural Approach

SINCO is a research effort to develop a software environment that contributes to the prevention and control of infectious diseases. This paper describes the system architecture already implemented where four important elements interact: (a) expert system (b) geographical information system (c) simulation component, and (d) training component. This architecture is itself a scalable, interoperable and modular approach. The system is being currently used in several health establishments as part of its validation process.

A case-based peer-to-peer framework for managing student models in Intelligent Tutoring Systems

Intelligent tutoring systems (ITSs) aim at providing personalized and adaptive tutoring to students by the incorporation of a student modeling component. In the near future, a very interesting scenario will appear when multiple tutoring systems exchange information in order to learn from its own experiences and improve their student modeling components. In order to get closer to such scenario, in this paper we present a case-based peer-to-peer multi-agent system for collaborative management of student models in ITSs. The goal of the system is twofold: first, to initialize the student model when a new student logs on the tutor system and second, to update the student model depending on the studentpsilas interaction with the system and exchanging this information with its peers. The quality of the system is evaluated in terms of its ability for searching similar cases (accuracy) tested under three different strategies. Our results show that increasing the system complexity (number of nodes and/or number of students) and using a committee strategy, the performance of the global system is improved by reducing network traffic, and preserving the quality of the solutions for the new students (cases).

Multi-agent Architecture for Intelligent Tutoring Systems Interoperability in Health Education

Intelligent Tutoring Systems (ITSs) base their interoperability on the interchange of domain knowledge about learning and teaching processes and about students. To enable the interchange of domain knowledge between ITSs and heterogeneous Health Information Systems (HIS) we propose a multi-agent architecture allowing sharing patients clinical data in Health Information Systems. The Health Level (HL7) is used as a standard messaging protocol for collecting the clinical patient data distributed over Health Information Systems.

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.

Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems

Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student

Digital circuit simulator via Internet

In this article, the authors present an application aimed at students who attend the subject of Digital Systems and want to make a continuous and progressive study of it. Thats why a logical simulator via Internet is being developed within the Internet portal of the subject of Digital Systems. It allows students to implement and to verify the practical part of the subject before the laboratory session. This simulator consists of an oscilloscope, a function generator, a power supply, in addition to several integrated circuits, which can be either combinational or sequential, or both.