Use this URL to cite or link to this record in EThOS:
Title: Developing the didactic operations for intelligent tutoring systems : a synthesis of artificial intelligence and hypertext
Author: Angelides, Marios
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 1992
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
This thesis is concerned with Intelligent Tutoring Systems. It investigates the architecture of an Intelligent Knowledge Based Tutoring System in terms of three knowledge models: that of the domain, the student and the tutor, and examines the interrelatedness and interconnectedness of these three knowledge models. Existing Knowledge Based Tutoring Systems are reviewed, and the relationship between their behaviour and architecture is analysed by evaluating them against Wenger's model of a didactic operation. Two such systems, PROUST, a tutoring system for Pascal program debugging skills, and micro-SEARCH, a tutoring system for mathematical transformations skills, are used in the study. This evaluation serves two purposes: to unravel the requirements for interrelatedness and interconnectedness between the three knowledge models in order to develop a true Knowledge Based Tutoring System with a full-scale didactic operation, and to uncover the limitations of the current generation of Knowledge Based Tutoring Systems and how they fail to fully encompass these requirements. On this basis the thesis goes on to propose a hybrid model made up of Artificial Intelligence and Hypertext concepts which helps to overcome the limitations of existing Knowledge Based Tutoring Systems. This model in particular addresses the requirements for the development of an Intelligent Tutoring Systems with a full-scale didactic operation. The model integrates Hypertext's explicit information nodes and linking properties with Artificial Intelligence's logical inferencing on knowledge representation schemes. The thesis finally shows how to use this model to design a generic Intelligent Tutoring System that supports a full-scale didactic operation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available