Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project

One of the goals of the knowledge puzzle project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the knowledge puzzle ontology learning tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.     

Enhancing Learning Objects with an Ontology-Based Memory

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.     

Improving the Behavior of Intelligent Tutoring Agents with Data Mining

This article presents a novel framework for adapting the behavior of intelligent agents. The framework consists of an extended sequential pattern mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human agents executing a procedural task. The proposed framework has been integrated within the CanadarmTutor, an intelligent tutoring agent aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. We present the results of an evaluation that demonstrates the benefits of this integration to agents acting in ill-defined domains.     

TOWARD LEARNING GRID INFRASTRUCTURES: AN OVERVIEW OF RESEARCH ON GRID LEARNING SERVICES

The Learning Grid refers to the promise of projects that pool together instructional materials on distant computers. The Grid provides a wide range of available and potential learning services and resources and does not simply refer to taking advantage of the multiplying effects of connectivity. It supports the personalized use of the collective intelligence provided by networked computers and supports the exchange, negotiation, and dialogue within and among virtual, evolutionary, and pervasive learning communities. This article provides an overview of papers from the first workshop on Grid Learning Services, which brought together researchers discussing their views of infrastructure, services, and resources. It also addresses several research questions, including: What are the relevant resources and services and how can they be identified or built? How do they rely on the basic open Grid service architecture? How can intelligent tutoring systems be built on the Grid? How do the performance, efficiency, usability, and the global ability of those services meet individual and collective users' expectations?     

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta-knowledge and rules in a given domain which then extend domain knowledge and serve as problem space, allowing the Intelligent Tutoring System to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.     

Inferring social network user profiles using a partial social graph

Classification problems refer to the assignment of alternatives to predefined categories. In this work we focus on ordered classification, called sorting, in which the predefined categories indicate several degrees of interest or suitability of alternatives for a certain user. The assignment of alternatives is based on multiple conflicting criteria. This multi-criteria sorting approach is specially interesting for recommender systems aimed at finding the most suitable alternatives for each user. First, we study the ELECTRE-TRI-B sorting method, which follows the outranking approach based on comparing the evaluations of alternatives with the profile limits separating the categories. The complexity of some recommenders systems requires the extension of the classical ELECTRE-TRI-B method to manage a taxonomical organization of the set of criteria. In this paper we consider a set of criteria in the form of a hierarchy. The intermediate criteria in such a hierarchy correspond to different aspects of the recommendation procedure, such as content, context or cost. At each of these criteria, a sorting problem must be solved. Therefore, we propose extending ELECTRE-TRI-B to handle assignments of alternatives on several levels of the hierarchy. A hierarchical procedure for sorting is proposed, called ELECTRE-TRI-B-H. Secondly, the paper explains the integration of ELECTRE-TRI-B-H into a recommender system of touristic activities related to wine, called GoEno-Tur. This system is developed for the region of Tarragona, Catalonia (Spain), which is a well-recognized area of wine and cava production.     

Integrating WWW resources in an Intelligent Tutoring System

In the process of developing an Intelligent Tutoring System (ITS), the construction of learning material to be considered in the teaching of a given domain is a crucial stage. Some of these materials are in hypermedia form. The main object of this paper is to show that HTML documents can be used as pedagogical material under the control of an ITS. A Web browser is used as the medium for the delivery of tests and the teaching of verbal information (declarative knowledge). We also show how to encapsulate a Web browser in order to use it under the control of the ITS. The language used is Smalltalk.     

THE GRID-E-CARD: ARCHITECTURE TO SHARE COLLECTIVE INTELLIGENCE ON THE GRID

Using the Grid for electronic learning can be considered a major innovation that increases the potential of the Internet for collaborative learning and transforms it into a gigantic high speed network of knowledge and services. Furthermore, with the refinement of peer-to-peer communication, the emerging network models will allow thousands of learners to access these solutions, regardless of their different computer systems. These solutions can be compared to storage devices that share learning resources according to the user' s needs. With the substantial increase of information nodes and the multiplicity of computers making use of this complex network, cognitive overload or transactional distance inherent to the technology can quickly become an obstacle to the learning process. Therefore, the Grid requires the development of appropriate learning tools and services. This paper presents an approach to reduce cognitive overload and transactional distance for a virtual learning community (VLC) on the Grid. This paper proposes a computer-Grid communication device called “Grid-e-Card.” Its goal is to bring users together according to their signature to share collective intelligence in a social context: the knowledge they have acquired, the objectives they wish to meet, or the learning services that correspond to their needs. This methodology is based on a set of P2P-agents who handle users' electronic portfolios (e-Portfolios) as knowledge prosthesis and exploit e-Learning qualification (e-Qualification) processes as aggregation methods to dynamically organize people in relevant virtual learning communities.