IWALT 2000 - International Workshop on Advanced Learning
Technologies 4-6 December 2000, Palmerston North, New Zealand.
Intelligent Learning Tools:
Adaptation to Non-stationary
Environments
Paul CRISTEA (chair)
“Politehnica” University of Bucharest
Spl. Independentei 313, 77206 Bucharest, Romania,
Phone: +40 -1- 411 44 37, Fax: +40 -1- 410 44 14
e-mail: pcristea@dsp.pub.ro
Panel papers:
Adaptation to Nonstationary Environments –
Learning and Evolution (Paul Cristea)
Cognitive Interface for Idea Processor (K. Kozuki,
N. Ashida, N. Nomura, T. Otsuka, A. Tsubokura, K. Tsushima)
Web-Based Learning and the Role of Context
(Ana Paula Afonso, António Dias de Figueiredo)
An adaptive Distance Learning Environment for Language
Teaching (Alexandra I. Cristea, Toshio Okamoto)
Eliminating the Distance Between Intelligent and Not
Intelligent Systems (Christina Metaxaki-Kossionides,
Georgios Kouroupetroglou, Stavroula Lialiou)
Discussion questions:
(1) How do you define an ILT? What should be its basic features,
what are the minimum requests?
(2) Can you share with us your experience in using ILTs?
(3) Can an ILT go over the role of only assisting a human tutor?
(4) Should the ILTs be used only for ODL, or also in standard
class teaching?
What would be their specific tasks in the two cases?
(5) How should an ILT evaluate the performance of a
human learner?
(6) What are the expectations for the immediate and for the
medium future?
By almost any measure, e-learning is booming.
•According to a recent U.S. government report, the demand for elearning is likely to leap from just 5% of all students in higher
education in 1998 to 15% by 2002.
•In the corporate sector, spending on employee training last year
totalled $2.5 billion, about 40% of which went to on-line education.
•Industry e-training is projected to double annually over the next
several years.
•The academic on-line market is also expected to move ahead
rapidly, reaching nearly $1.6 billion by 2002. What many educators
are realizing is that e-learning is a trend they can no longer ignore.
Most popular e-learning packages:
• Blackboard's Courseinfo (http://www.blackboard.com)
- on-line course mananagement system that uses templates.
• Lotus LearningSpace (http://www.lotus.com/home.nsf/welcome/learnspace)
- targeted at corporate users;
- sold through IBM Corp.'s e-learning business unit,
- IBM Mindspan Solutions.
• WebCT (http://www.webct.com)
- low cost asynchronous course delivery and management system;
- developed at the University of British Columbia,Vancouver, B.C., Canada;
- sold through Universal Learning Technology, Peabody, Mass.
• Topclass (http://www.wbtsystems.com)
- WBT Systems, Waltham, Mass.;
- the most mature product on the market.
Customized e-learning platforms for colleges
and training organizations:
• DigitalThink (http://www.digitalthink.com)
• Convene (http://www.convene.com)
• eCollege (http://www.ecollege.com)
• Professional qualification is no longer a
life-long achievement
• Complex knowledge and skills have to
be transmitted and acquired efficiently
• Open and Distance Learning will play a
continuously increasing role.
• Intelligent educational tools can bring
the flexibility and adaptability required
to actively support the learner.
Basic paradigms:
• Intelligent Human-Computer Interaction
• Computer-Supported Cooperative Work (CSCW)
Learning in the system: Cooperative learning by interaction between
student and tutor/expert or inside the group of learners
Organization: Group of learners assisted by artificial agents with
active role in the learning process.
Tutor: Human or artificial agent
Structural features:
• Set of tools to assist the learner at several levels of the knowledge
acquisition process.
• Personalised model of the trainee
Combine the traditional style of teaching
with the problem-based style:
• learning by being told,
• problem solving demonstration,
• problem solution analysis,
• problem solving,
• creative learning
Knowledge transfer
Lea rning
by being
t o ld
Pro blem
so lving
demo
Skill development
So lut ion
a na lysis
Pro blem
so lving
Level of learner’s active participation
Crea t ive
lea rning
WWW
In f o r m at i on
A gen t
Tut or
A gent
Tutor
Agent KB
Problem Solving
Knowledge Base (KB)
Human
Tutor
Human
Learner 1
Human
Learner n
Tut or A ssi st ant
A gent
Tutor KB
1 st Lear ner 's
Per sonal A gent
Learner1 KB
nt h Lear ner 's
Per sonal A gent
Learnern KB
Legend
Agent-agent interaction
Agent access to KB
Human-human communication through network
Human access to KB
Human-agent interaction
Search of info sources
Tut or A gent
Module for accessing learning
resources and managing
interactions:
- Problem Solving KB
- Selection of relevant
knowledge
- Coordination activities
Communication
Module
Control Module
Module to select
learning modalities
and to adapt to
learner profile
Problem
Solving
Knowledge
Base (PSKB)
Module to respond
to learner requests
and needs
Tutor Agent KB:
 Knowledge to access
PSKB
 Methodological
 Knowledge on how to
adjust to learner
profile
Other
agents
Tut or A ssi st ant A gent
Module for accessing learning
resources and managing
interactions:
- Problem Solving KB
- Coordination activities
Communication
Module
Control Module
Module to extract:
- tutoring knowldege
- tutoring strategies
- creative learning
experiences
Problem
Solving
Knowledge
Base (PSKB)
Module responsible
for monitoring tutor
actions and guiding
Tutor KB:
 Knowledge to
retreive elements
from PSKB
 Training history
 Elicited tutoring
knowledge
Other
agents
Learner Personal A gent
Module for accessing learning
resources and managing
interactions:
- Problem Solving KB
- Coordination activities
Communication
Module
Control Module
Module to develop
the learner profile
Problem
Solving KB
Module responsible
for monitoring
learner actions and
requests
Learner KB: learner
history and learner
profile
Other
agents
No purely empirical approach to modelling.
Even the definition of attributes/features &
the selection of the relevant ones in a given context
are actually theory driven, explicitly or not.
Prototype model of the learner
• Encodes general theoretical knowledge in the field of learning.
• Can not be used directly in practice - rigid and biased:
• Large variability in human personality and in human behaviour,
• The essential traits are context-dependent.
Customised model by using empirical data - sets of examples
collected for the given user, while interacting with the system.
New refined theory
If tuning parameters can not adapt the model to user's profile,
new features are extracted from data and added to the model.
No systematic way to empirically identify the domains of the
feature space that are not properly represented in a set of examples.
• The available collection of examples is never large enough to cover
all the possible classes in an unbiased manner, to avoid spurious
correlation when elaborating a model.
• Small sets of exceptions may be poorly represented or even ignored.
The underlying theory
• helps eliminate irrelevant features,
• guides the selection of relevant examples to scan of the input space,
• gives confidence in the solutions produced.
A purely theoretical approach may be brittle, i.e.,
• can yield dramatically incorrect results for exceptions,
• scores of instances that fall in the limits of validity domain are
treated correctly (abrupt degradation).
Exhaustive theories may become intractable
• The domain of validity must be restricted.
• Compromise scope - accuracy.
Combined use of theoretical knowledge and experimental results allows:
• Incomplete and/or incorrect theoretic knowledge,
keeps the model in the range of an acceptable approximation.
• Incomplete or noisy experimental data
inherent ability to recover from errors.
The user model being developed uses a hybrid approach:
• Artificial Intelligence (AI) -- symbolic representation of theory,
• Neural network (NN) -- sub-symbolic representation of data.
NN has the ability to represent "empirical knowledge",
but behaves almost like a black box:
• Information expressed in sub-symbolic form,
not directly readable for the human user
• No explanation to justify the decisions in various instances,
forbids the direct usage of NNs in learning/teaching and
safety critical areas
• Difficult to verify and debug software that includes NNs.
Extraction of the knowledge contained in an NN allows the portability
• to other systems in symbolic (AI) and sub-symbolic (NN) forms,
• towards human users.
AI and NN approaches are complementary in many aspects
• can mutually offset weaknesses and alleviate inherent problems,
• able to exploit both theoretical and empirical data - hybrid aproach,
• efficient to build a fault tolerant and adaptive model,
• help discover salient features in the input data.
First phase. The system operates using statistics about:
• which buttons were selected by the lerner when using the system,
• in which order,
• which error messages have been generated.
The system is trained to use this input to offer advice in the form of
• access to some additional data and information,
• additional reading,
• recommend or trigger an interaction with the human tutor.
Subsequent phase. The system uses:
• error databases,
• special interest databases,
• preference databases,
including the input from a human tutor.
The output helps identifying some profile of the user,
defined roughly by the set of classes the user belongs to.
This influences the future interaction of the system with the user,
e.g., changing the type and level of the exercises presented to the user.
Next step. The system includes some voluntary feedback learners,
offered to all the other learners, to help conveying original ideas
and generate groups of interest.
Increase of tutor "productivity“. The system is a useful assistant,
not a replacement of the human tutor.
The work done traditionally by two or three tutors could be
accomplished in this approach by only one assisted tutor.
The basic contribution of this research is twofold:
• Identification of several Learning Modalities that combine
traditional teaching with “problem-centred” learning
to better motivate the student and to increase the efficiency
of the learning process,
• Conception of a Collaborative Distance Learning System in which
human and artificial agents collaborate to achieve a learning task.
The Tutor Agent tries to replace partially the human teacher, in
assisting the learners at any time of their convenience.
The development of the learning system is a collaborative effort
to develop a novel intelligent virtual environment for ODL at
“Politehnica” University of Bucharest.
The system is currently under development; several components
written in Java are already functional.
To test the system, we are concurrently developing learning materials on:
• Sorting Algorithms,
• Resolution Theorem Proving,
• Neural Networks,
• Advanced Digital Signal Processing.
The distributed solution has the advantage of creating an ODL environment
that can be joined by any interested learner.
The system is an effective response to the
• the increased demand for cooperation and learning in today's open
environments, academic and economic,
• the necessity of developing effective learning tools that can be smoothly
integrated in the professional development process and with company work.
Care is taken to prevent such an approach to generate an "elitist" system.
The system is designed to enhance the specific features of each user,
without increasing the differences between users in what concerns the level
of understanding or the ability to creatively use the acquired knowledge.