TABLE
OF CONTENTS
Introduction
CHAPTER 1: INTELLIGENT
TUTORING
Traditional Computer Aided Learning Systems
Intelligent Tutoring System Architectures
The expert
module
If - Then
Rules
If - Then Rules with uncertainty measures
Semantic Network representations
Frame based representations
The
student diagnosis module
Student
model architectures
Overlay
student models
Differential student models
Perturbation student models
Student
model diagnosis
Performance
measuring
Model
tracing
Issue
tracing
Expert
systems
The curriculum and diagnosis module
Examples
GUIDON
WEST
LISP TUTOR
BUGGY
Recent
developments and Summary
CHAPTER 2: HYPERTEXT AND ADAPTIVE HYPERTEXT
Hypertext
The
development of hypertext
Architecture and structure of hypertext
Advanteges
of hypertext
Problems
with hypertext
Hypertext
adaptation
Navigational
adaptation
Navigational adaptation techniques
Presentational
adaptation
User modelling and Adaptive Hypertext
Adaptive
hypertext systems
ISIS-TUTOR
ANATOM-TUTOR
KN-AHS
HYPERFLEX
Summary of reviewed adaptive hypertext systems
CHAPTER 3: MACHINE LEARNING TECHNIQUES
Machine Learning
Rote Learning
Induction
The
focusing algorithm
The application of focusing in Intelligent Tutoring
Problems
with focusing
Concept Learning and Decision Trees
The
Classification algorithm
The ID3
algorithm
The application of ID3 in Intelligent Tutoring
ID4
algorithm
ID5
and ID5R algorithms
Explanation based learning methods
The application of EBL in Intelligent Tutoring
Clustering
Statistical
Clustering
Conceptual
Clustering
Kohonen
Net Clustering
Analogy
Transformational
analogy
Derivational
analogy
Authored by
Serengul Smith
E-mail to:serengul1@mdx.ac.uk
School of
Computing Science Middlesex University
Revised: September 1998