EVOLVING GRAPH BASED KNOWLEDGE SPACE MODEL FOR TUTORING SYSTEMS

Proceedings of the 4th International Workshop on Computational Intelligence and Information Technologies, R. S. Stankovic and C. Moraga (Eds.), 2003

Three knowledge-modeling techniques that have recently started to attract the attention of researchers and developers in the domain of Intelligent Tutoring Systems (ITSs) are in usage recently. These techniques are hierarchical modeling and patterns, interoperable and reusable software components, and ontology. All three of them have been used in developing a model of ITSs called GET-BITS (GEneric Tools for Building ITSs). The GET-BITS model has been used throughout the paper in order to illustrate the techniques. The major goal of the paper is to show how these three techniques can be used to make the internal organization of ITSs more natural, more flexible, and more robust, to enhance their design, and to improve their performance. Important modules of any intelligent tutoring system, like domain knowledge, pedagogical knowledge, student model, and explanation strategies, are discussed extensively in the context of the three modeling techniques and the GET-BITS model. Experience with using GET-BITS as the basis for building practical applications shows how the processes of computer-based tutoring and learning based on the GET-BITS model are much closer to human-based instruction. From the design perspective, major advantages of using hierarchical modeling, software components, and ontologies in developing practical ITSs include enhanced modularity, easy extensions, and important steps towards knowledge sharing and reuse.

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006

In this paper, we present The Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge acquisition for knowledge-based systems and especially for intelligent tutoring systems. We present a new content model, the Knowledge Puzzle Content Model, that aims to create Learning Knowledge Objects (LKOs) from annotated content. Annotations are performed semiautomatically using natural language processing algorithms. These LKOs are then aggregated in an Organizational memory (OM) which serves as a knowledge base for an Intelligent Tutoring System (ITS).

Domain knowledge (DK) is a basic part of an intelligent tutoring system (ITS). DK usually includes information about the concepts the ITS is dealing with and the teaching material itself, which can be considered as a set of learning objects (LOs). LOs are described by a data set called learning object metadata. Concepts are usually organized in a network, called a concept network or map. Each concept is associated with a number of LOs. In this paper, we present a tool for managing both types of information in DM: creating and editing (a) a concept network and (b) learning object metadata. Additionally, the tool can produce corresponding XML descriptions for each learning object metadata. Existing tools do not offer all the above capabilities.

2006

Intelligent educational systems are knowledge-based systems (KBS) they can be developed by a generic knowledge-based system development methodology. In this paper, we present an ontology-based approach for formalizing different knowledge types. The formalism is based upon conceptual graphs. A priority concern to all research work in adaptive education is that of finding an appropriate representation for pedagogical knowledge. For implementation, we use the CoGITaNT environment (Conceptual Graphs Integrated Tools allowing Nested Typed graphs), a library of C++ classes (open-sources, developed by LIRMM CNRS, France) allowing the development of applications based on the CG knowledge representation scheme.

We present in this paper a tutorial system for basic concepts in Knowledge Representation. For tutorial purpose, we have chosen a Conceptual Graph Theory, proposed by Sowa as a universal knowledge representation. The tutor is based on visual presentation and use of hypermedia techniques.

Introduction of computers has induced an increased interest of teachers in their wider use. Hence, the teachers have become interested in taking an active role in designing educational software, and not being only passive users of these tools. However, the advancement of AI methods and techniques makes understanding of Intelligent Tutoring Systems (ITS) more difficult, so that the teachers are less and less prepared to accept these systems. As a result, the gap between the researchers in the field of ITS and the educational staff is constantly widening. Also, building an intelligent tutoring system (ITS) requires the ability to model and reason the domain knowledge, human thinking and learning processes, and the teaching process. Acquiring and encoding this large amount of knowledge is difficult and time-consuming. We have been searching for efficient ways to do these knowledge engineering tasks. This paper describes our efforts toward developing uniform data and control structures that can be used by a wide circle of authors, e.g., domain experts, teachers, curriculum developers, etc., who are involved in the building of ITS. One of our goals is to build tools that will enable experts (teachers) to use the computer directly. These tools are a model of an application of object-oriented software design methodology for designing ITS knowledge bases and manipulating their contents. An object-oriented model of ITS knowledge bases has been developed, and its two aspects: the design and organization of knowledge bases, and communication between the knowledge base and another module of an ITS or an ITS building tool, is briefly described in the paper.

In this paper, we present an approach to student's evaluation in a well-defined domain based on a semantic network. A similarity matrix based on the semantic memory structure of humans is used to build a semantic distance model in order to describe an assessment technique to evaluate the student's state of knowledge. Our aim is to facilitate a deeper conceptual understanding of domain principles. We are developing a new student model including an assessment module with DistSem model.

2000

This paper presents a knowledge model (KM) of an ITS with Precalculus as the domain knowledge. The Knowledge Model is represented by Semantic Net, organized as interconnected knowledge entities in the form of nodes. The comprehensiveness of the ...

2008

As knowledge becomes a crucial asset to organization’s survival, an efficient knowledge management policy should be set up and should result into an organizational memory. Similarly, an effective eLearning program must be implemented in the organization and exploit the organizational memory to manage competence evolution. In this paper, we propose a framework that integrates knowledge management and Intelligent Tutoring Systems. This framework exploits the organization members’ collective competence and knowledge to encourage knowledge sharing and reusability across the organization. It also provides better learning sessions through the use of an Intelligent Tutoring System.

2000

This chapter describes three modeling techniques that have recently started to attract the attention of researchers and developers in the domain of intelligent tutoring systems (ITSs). These techniques are: hierarchical modeling, interoperable and reusable software components, and ontologies. All three of them have been used in developing a model of ITSs called GET-BITS (GEneric Tools for Building ITSs). The GET-BITS model has been used throughout the chapter in order to illustrate the techniques. The major goal of the chapter is to show how these three techniques can be used to make the internal organization of ITSs more natural, more flexible, and more robust, to enhance their design, and to improve their performance. Important modules of any intelligent tutoring system, like domain knowledge, pedagogical knowledge, student model, and explanation strategies, are discussed extensively in the context of the three modeling techniques and the GET-BITS model. Experience with using GET-BITS as the basis for building practical applications shows how the processes of computer-based tutoring and learning based on the GET-BITS model are much closer to human-based instruction. From the design perspective, major advantages of using hierarchical modeling, software components, and ontologies in developing practical ITSs include enhanced modularity, easy extensions, and important steps towards knowledge sharing and reuse.