Jaime Ramírez

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the students knowledge, but rather they should reflect, as faithfully as possible, the students reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. Its focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the students knowledge state - especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the students features and a high flexibility for its integration in varied ITSs.

Intelligent virtual environments for training: an agent-based approach

In this paper we propose an architecture for the development of Intelligent Virtual Environments for Training, which is based on a collection of cooperative software agents. The first level of the architecture is an extension of the classical Intelligent Tutoring System architecture that adds to the expert, student, tutoring and communication modules a new module which is called World Module. Several software agents compose each module. Moreover, the proposed architecture includes agents able to simulate the behavior of human students and tutors, as well as agents able to plan the procedures to be taught (given an initial state and a desired final state) prior to the tutoring process.

A network of ontology networks for building e-employment advanced systems ☆

This paper presents the development of a network of ontology networks that enables data mediation between the Employment Services (ESs) participating in a semantic interoperability platform for the exchange of Curricula Vitae (CVs) and job offers in different languages. Such network is formed by (1) a set of local ontology networks that are language dependent, in which each network represents the local and particular view that each ES has of the employment market; and (2) a reference ontology network developed in English that represents a standardized and agreed upon terminology of the European employment market. In this network each local ontology network is aligned with the reference ontology network so that search queries, CVs, and job offers can be mediated through these alignments from any ES. The development of the ontologies has followed the methodological guidelines issued by the NeOn Methodology and is focused mainly on scenarios that involve reusing and re-engineering knowledge resources already agreed upon by employment experts and standardization bodies. This paper explains how these methodological guidelines have been applied for building e-employment ontologies. In addition, it shows that the approach to building ontologies by reusing and re-engineering agreed upon non-ontological resources speeds the ontology development, reduces development costs, and retrieves knowledge already agreed upon by a community of people in a more formal representation.

SEEMP: An Semantic Interoperability Infrastructure for e-Government Services in the Employment Sector

This paper presents SEEMP, a marketplace to coordinate and integrate public and private employment services (ESs) around the EU Member States. The need for flexible collaboration in the marketplace gives rise to the issue of interoperability in both data exchange and share of services. SEEMP proposes a mixed approach that relies on the concepts of services and semantics. SEEMP approach combines Software Engineering and Semantic Web methodologies/tools in an infrastructure that allows for a meaningful service-based communication among ESs.

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the students behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the students knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.

Semantic Verification of Rule-Based Systems with Arithmetic Constraints

The aim of this paper is to show a method that is able to detect a particular class of semantic inconsistencies in a rule-based system (RBS). A semantic inconsistency is defined by an integrity constraint. A RBS verified by this method contains a set of production rules, and each production rule comprises a list of arithmetic constraints in its antecedent and a list of actions in its consequent. An arithmetic constraint is a linear inequality defined in the real domain that includes attributes, and an action is an assignment that changes an attribute value. As rules are allowed to include actions of this kind, the behaviour of the verified RBS is non-monotonic. The method is able to give a specification of all the initial fact bases (FB), and the rules from these initial FB that would have to be executed (in the right order) to cause an integrity constraint to be violated. So, the method builds an ATMS-like theory. Moreover, the treatment of arithmetic constraints is inspired by constraint logic programming.

A SOFTWARE ARCHITECTURE FOR INTELLIGENT VIRTUAL ENVIRONMENTS APPLIED TO EDUCATION

This paper describes the software architecture that has been designed as a model for the application of Intelligent Virtual Environments to training activities. CORBA has been used as the middleware to integrate a graphical and interactive environment developed in OpenGL and Visual C++, with a cooperative multi-agent system developed on top of the JADE platform.

Checking the consistency of a hybrid knowledge base system

This paper concerns a method to verify the consistency of a hybrid knowledge base system. We assume the knowledge base of the verified system supports the representation of production rules and frame taxonomies. Moreover, the knowledge base can also be used to represent non-monotonic and uncertain reasoning. For the purpose of the verification, an ATMS like theory is created by simulating the deductive process needed to deduce an inconsistency. Next, the ATMS like theory is processed to compute a specification of the initial fact base that allows for the execution of a deductive tree that leads to the inconsistency.

Automated planning and replanning in an intelligent virtual environments for training

The application of Artificial Intelligence (AI) planning techniques to the development of Intelligent Tutoring Systems (ITS) has focused mainly on instructional planning, in settings where the initiative is taken primarily by the system. 3D Virtual Environments (VE) have emerged in the last years as a good means to apply a case-based training approach, placing a more active role on the student. Here AI planning turns out to be an interesting solution for the dynamic resolution of the problems (cases) that are posed to the student. These environments allow the students to navigate through and interact with a virtual representation. This paper describes MAEVIF, a platform for the development of intelligent virtual environments for training (IVETs) whose architecture is based on a collection of cooperative software agents. The role of AI planning in the teaching-learning approach followed by MAEVIF is described, with two main planning services: the generation of a plan as an ideal solution to the case, and the evaluation of the effect of the students actions during their resolution of the case.

SEEMP: A Networked Marketplace for Employment Services

Human capital is more and more the key factor of economic growth and competitiveness in the information age and knowledge economy. But due to a still fragmented employment market compounded by the enlargement of the EU, the human resources are not effectively exchanged and deployed. The business innovation of SEEMP1 develops a vision of an Employment Mediation Marketplace (EMM) for market transparency and effic ient mediation. Its technological innovation provides a federated marketplace of employment agencies through a peer-to-peer network of employment data and mediation services. In other words, the solution under development is a de-fragmentation of the employment market by a web-based collaborative network. The SEEMP-enabled employment marketplace will strengthen the social organization of public employment administration, maximize the business turnover of private employment agencies, improve citizens’ productivity and welfare, and increase the competitiveness and performance of business.