1
Intelligent Tutoring Systems : A Multidisciplinary Approach
M. Kaltenbach*, C. Frasson**
** Université de Montréal
Département IRO
CP 6128 Montréal
(Québec) H3C 3J7
** Bishop's University
Department of Management
and Information Science Lenoxville
(Québec) J1M 1Z7
frasson@iro.umontreal.ca
kaltenbach@iro.umontreal.ca
Abstract
Research in Intelligent Tutoring Systems is evolving. It results from a multi-disciplinary
effort to create a computer based technology to genuinely facilitate learning. In the present
article we adopt a serie of perspectives, each one aiming at clarifying a particularly
important issue in the design of computer based educational systems. We successively
examine the role and the contributions of artificial intelligence and of cognitive science to
the field. Then, we present some pragmatic and recent orientations of ITS research
1 Introduction
2 AI to the rescue of CAI
a - The role of AI
b - GUIDON : a tutoring system over an expert system
c - Reconfiguration of tutoring systems derived from expert systems
d - ITS as a test bench for AI
3 Cognitive science contributions to ITS research
4 ITS research as a pragmatic, interdisciplinary research domain
5 Conclusion
2
1 Introduction
In the seventies and early eighties computers have held the promise of offering a
worthwhile complement, if not an alternative to traditional teaching methods. Word
processing, calculation facilities and a variety of graphic tools have had an impact on the
definition and use of knowledge. However, among those concerned in the development of
teaching systems there is some disatisfaction when the present achievements are compared
to the initial expectations.
This phenomenon is not unique to Education. Similar disillusionments have been
encountered in areas such as the automated translation of human languages and computer
scene analysis. By now we realize that tasks that appear simple to humans can be extremely
difficult to automate and conversely computational tasks of a complexity that defies
humans are routinely performed by machines [Winston, 1984].
Also there has been some misrepresentation of the various kinds of expertises needed to
produce effective teaching systems; it is not sufficient to pull together the talents of a wide
range of specialized experts; the integration of these expertises is a real challenge. With
hindsight it turns out that each expert expected much more of the others than each could
deliver. In the seventies and eighties the enthusiasm for AI and the new field of Cognitive
Science predicted a radical departure from traditional teaching methods. Nothing of the sort
has happened yet and opportunities for major changes are relegated to a more distant future.
Imperceptibly however, the field of computer based teaching and learning is maturing. The
first computer-aided instruction (CAI) systems have shown their limits and the need to
develop intelligent tutors enabling a machine to understand both the concept to be taught
and how the student can learn that concept [Woolf, 1987]. The last five years have
3
witnessed researchers settling for more reasonable, less ambitious goals and closer
cooperation between contributing areas of expertise. The computation experts have become
more aware of the limitations of their computational models and the cognitive scientists
have come to realize that human dimensions cannot be captured entirely in their variety of
kinds and degrees in formal models however complicated they may be.
This understanding leads to more respect for students, no longer seeing them as receptacles
into which knowledge is to be poured [Brown, 1983; Brown & Burton, 1987]. The goal
now is to help students recreate knowledge by themselves by using a variety of supporting
computer tools, capable of offering guidance and/or advice and facilitating collaborative
work between students (and teachers). In the process the traditional distinction between
student and researcher is subsiding.
In the present article the reader is invited to a spectral analysis of this multi-disciplinary
effort to create a computer based technology to genuinely facilitate learning and research.
To do so we adopt a serie of perspectives, each one aiming at clarifying a particularly
important issue in the design of computer based educational systems.
The first section deals with the problem of knowledge representation and multiple
contributions of Artificial Intelligence (AI); specifically we shall try to answer the question
of what structure is adequate for what kind of knowledge. The second section is an
assessment of the contributions of Cognitive Science and Educational Psychology. The
third section considers the impact of empirical developments in Graphical User Interfaces,
Hypertext and of the spreading of a common computer "culture" with the result that the
development of effective systems for learning may no longer be reserved for a few
theoreticians and high power programmers.
4
2 - AI to the rescue of CAI
As soon as computer became largely available in educational settings in the late sixties and
early seventies, they have been seen as offering a possible solution to the problem of
increased mass education. The first Computer Aided Instruction systems implemented the
strict didactic approach of dividing the subject matter to be taught into small units that had
to be seen in sequence on a video screen by the student. The computer provided the
opportunity to test the student on each point made so as to control his/her progression in the
sequence. The computer also made possible the creation of non-linear curriculum structures
that could be traversed in a variety of ways.
With the help of an Authoring tool, the author of a lesson would decide on including
branching points and provide explanations to be presented in response to student errors.
However the limits of this approach were soon realized. As the coverage and complexity of
the domain knowledge to be taught increased, it became prohibitively costly to produce
CAI lessons; also it became important to have ways of taking note of what nodes had been
visited by a student and at what level of mastery, in order to direct him wisely in the
network.
Artificial Intelligence (AI) came into play as a possible answer to these practical problems.
However without concrete examples of what AI could contribute the response from the
pedagogical community, who had heavily invested in CAI systems, was rather cautious.
This led to a raising of claims by researchers in AI. The new systems, now called Intelligent
Tutoring systems or ITS, would approach the pedagogical skills of human teachers,
separating knowledge of the student from knowledge of the domain and also from teaching
stategies.
5
The work was divided among two often overlapping groups of researchers. One group was
represented by computer scientists keen on finding adequate knowledge structures to model
in the computers, and so expanding the field of AI. The other group included cognitive
psychologists keen on testing the psychological validity of the AI models, with some claim
of influencing the development of these models. Their work is currently known as
Cognitive Science.
As far as ITS research is concerned it is difficult to separate the AI and Cognitive Science
components, making it a truly interdisciplinary activity. This is evidenced by the number of
University departments in the US that have merged AI researchers with psychologists. In
order not to get lost in the details of each contributing field we now examine what seems
the fundamental tenets of each approach with regards to the goal of achieving effective ITS.
a) the role of AI
The avowed aim of Artificial Intelligence is to achieve intelligent behavior [Harmon &
King, 1985]. However the concept of intelligence itself is not easy to define. Alan Turing
[Turing, 1964] avoided the problem of defining intelligence by proposing a test of machine
intelligence. The test was whether one could discriminate between machine and human
performance particularly in the computer chess game. At present if we compare the
performance of an average chess player with that of a medium price computer chess game,
the machine wins consistently.
Thus it seems that intelligence is not just information processing. Some humans with great
information processing abilities may appear intelligent and yet, when they are placed in
really unusual circumstances they are unable to break the frontiers of their established
thought patterns. Somehow intelligence has to do with invention and judgement. Invention
is related to the willingness to play with concepts, to distort them from their established
definitions and defy academic schools of thought. However an unruly conduct of one's mind
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would not lead one very far if fierce judgement was not exerted to weed out lines of thought
that stray too far from fixed goals, priorities and general values. Judgement involves the
individual in his/her totality.
In short intelligence relies on a mixture of order and disorder. So far, with the notable
exception of D. Lenat and his work on AM and Eurisko [Lenat & Brown, 1984], AI has
been successful with ordering knowledge and improving information processing
capabilities to automate very specific reasoning processes. The promises of the future are
exciting but the goal of achieving real intelligence is as remote as ever [Dreyfus]. What we
have instead is great tools for assisting human intelligence. In the following we describe the
application of these tools to the design of ITS systems. Then we comment on how they
could be improved to perform their role of assistance better.
Designers of CAI systems have been prompt to adopt the AI data structures and inference
mechanisms. It follows that a rather close parallel can be made between the evolution of IA
paradigms and that of ITS. The first CAI systems used the network formalisms prevalent
in the IA of the time, [Carbonell, 1970, Brackman, 1978, VanLehn & Brown, 1980].
Network representations may be adequate to represent the subject matter to be taught.
However other types of expertise are needed to conduct a tutoring session but are difficult
to implement by extending the network formalism. Figure 1 illustrates an architecture of the
different components of an ITS. Several experts modules try to interact in order to apply the
best tutoring session to the student through an interface. The student actions are done in a
microworld, an environment in which general and specific knowledge can be shown (for
instance electronic circuits or equations in physics).
The domain expert refers to the knowledge of the subject matter to be taught. The domain
is organized in a curriculum, a structure including all the knowledge elements linked
together according to pedagogical sequences. Each knowledge unit can be more or less
7
detailed (granularity level) and the curriculum can be pratically organized according to
various structures such as hierarchies, semantic networks, decision trees, procedural
instructions and production rules. The crucial problems deal with the capability to gradually
adapt the explanation of the reasoning to the learner.
The tutor is an expert in pedagogy. It contains tutorial rules and strategies for presenting the
knowledge, explaining the examples, adapting the dialogue to the learner. Tutoring
strategies can include coaching, mixed initiative, discovery approach, Socratic dialogue,
case based reasoning, ...
The student model is a knowledge base on students's knowledge [VanLehn, 1988]. This can
include missing concepts, misunderstandings, mental states obtained in problem-solving
situations (final or intermediate states), skill mastery. The model is generally described by a
data structure joined with a diagnosis module able to find the student's current state of
knowledge. The diagnosis process is fired regularly when a student wants to advance to the
next topic of the curriculum or when the ITS has to choose the most suitable exercice
according to the level of mastery of the student. The student model is periodically consulted
before issuing an action.
8
DOMAIN EXPERT
STUDENT MODEL
CURRICULUM
INTERFACE
PLANNER
BLACKBOARD
MICROWORLD
TUTOR
Figure 1 Architecture of an ITS
The planner is a part of the system that schedules the tutoring actions. Communications
between the different modules can be assumed by blackboard techniques [Murray, 1989].
The Expert System formalism seemed then well adapted to this need for modularity and
flexibility. The most well known effort in adapting and extending the Expert Systems
methodology to ITS is represented by the GUIDON program. The idea was to complement
the domain expert module represented by the MYCIN expert system (to diagnose infectious
diseases) by others modules adapted to student guidance in a tutorial session.
b) GUIDON : a tutoring system over an expert system
GUIDON was intended to be a tutoring system extracted from the MYCIN's expert system.
GUIDON used the MYCIN's knowledge base and the teaching strategy adopted was based
on a case method [Clancey, 1984, 1987]. Tutorial rules were built by successive case
studies and a mixed-initiative dialogue is activated on a list of cases examined by the
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student in a real problem-solving situation. In GUIDON, the student can obtain additional
details on data, proposes hypotheses, makes a diagnosis and a prescripion, and GUIDON
answers student queries by giving data, warning or advices, suggesting steps, as a Socratic
tutor does. However, these tutorial strategies are not flexible enough to be adapted to other
domains and experimentation of GUIDON led to several problems and lack of efficiency.
According to Clancey (Clancey, 1984, p.269) the dialogue management expert is not able to
adapt the level of discourse to different granularity levels for domain rules. An important
weakness of both MYCIN and GUIDON is that they suppose that the student has the
knowledge of all the technical vocabulary terms used by specialists. One of MYCIN 's
problems was that:
- the system was designed by specialists in the field, and intended for specialists,
- the rules represent a compiled expertise but this compiled form makes them
incomprehensible for students. It is also difficult to update and maintain the integrity of the
knowledge base
These drawbacks raised fundamental issues to obtain before an expert system can be used
for tutoring purposes : how to articulate the reasoning strategy to satisfy student behavior ?
How to structure the knowledge base to realize the previous objective ?
c) Reconfiguration of tutoring systems derived from expert systems
It was necessary to restructure MYCIN due to the implications derived from tutoring
systems (knowledge separation and need for cognitive models). In fact, the design of
NEOMYCIN was a complete reconfiguration of MYCIN's knowledge base in order to
establish a model of "diagnostic thinking" [Clancey, 1984]. One of the objectives of
NEOMYCIN was to articulate the reasoning strategy, by separating strategic knowledge
10
from domain facts and rules. The reasoning strategy is expressed in terms of metarules and
tasks. The reasoning process implemented in MYCIN used a backward search, a strategy
generally used by human experts and an efficient strategy when the set of possible
conclusions is small. Restructuration of MYCIN led to a domain-independent set of rules
about how to use the rules. The system can then move forward, proposing hypotheses.
According to Clancey and Letsinger [Clancey & Letsinger, 1981] " it is precisely
NEOMYCIN's forward, non-exhaustive reasoning and management of a space of
hypotheses that makes its strategy more human-like".
Numerous articles have been produced by Clancey on the reconfiguration of MYCIN,
leading to GUIDON2. The main issues that can be found in [Clancey, 1984, Clancey, 1987,
Wenger, 1987] are essentially based on the fact that the problem was not a question of
quantity of knowledge but rather a question of organization of knowledge, decompiling
expertise and articulating the reasoning strategy. In this scope, a set of metarules that
represent a reasoning strategy (organized in a hierarchy) was expressed in terms of tasks on
specific information. The new system was able to propose hypotheses from the data
(moving forward) and manage sets of hypotheses into classes; in particular, an heuristic
classification of abstract classes of problem-solving methods led to the HERACLES
system.
GUIDON 2 integrates all this aspects, including a set of instructional modules such as:
GUIDON-WATCH, an interface allowing the inspection of the reasoning strategy used by
HERACLES, GUIDON-MANAGE, a problem-solving environment in which the student
can see models of patients, GUIDON-DEBUG, a real learning module that allows the
student to critique problem-solving sessions, modifying the knowledge base (introducing
bugs or deleting knowledge) and see the consequences of his actions.
d) ITS as a test bench for AI
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Since the earliest developments of expert systems in the late seventies, AI research has
made significant progress toward achieving better ways of organizing factual and reasoning
knowledge along hierarchical and/or modular structures. ITS research has been prompt to
make use of new AI paradigms. Aside from work on GUIDON, many proposals have been
made to represent domain knowledge and structures for tutorial control hierarchically,
[Bonar & al, 1986], [Woolf, 1987], [Lajoie & Lesgold, 1989].
Trends toward modular, decentralized ITS are represented by blackboard architectures
[Murray, 1989]. A blackboard is a working memory shared by several knowledge bases and
sometimes an agenda scheduler determines which knowledge base gets to use the
blackboard next. The possible use of an actor formalism and notion of "intelligent agents"
are currently under way [Hewitt, 1991, Gauthier, 1992]. Agents can be considered as
subsystems able to cooperate and arrange their activities and knowledge with the object of
executing a precise task. In [Chan, 1992] a curriculum tree is presented as a tree of
knowledge-based systems working together. Three agents (representing the computer
teacher, the computer companion and the human student) communicate through a
blackboard via a simple agent scheduler which controls the agents when look at the
blackboard and execute. Each agent is a set of rules of behavior modeling the behavior of
the agent. This learning model is called "Learning Companion System".
A related evolution is represented by attempts to capture related informations into
somewhat selfcontained units (chunks), resulting in ITS using the frame formalism [Schank
& Edelson, 1990], object orientation [Bonar & al, 1986], and case-based reasoning [Bloch
& Farell, 1988, Farand & al, 1990].
A quick survey of recent ITS work shows that ITS designers are quite pragmatic in
combining the various formalisms. Thus ITS research offers a valuable testing ground for
12
the various AI formalisms. Later we shall describe from the viewpoint of Cognitive
Science how AI models are used in studies for a better understanding and support for
student learning processes.
3 - Cognitive Science contributions to ITS research
Cognitive Science is the scientific study of human knowledge acquisition, knowledge
organisation and reasoning processes. What differentiates it from broader psychological
research is the reliance on building computational models in order to test theories about the
workings of the human mind.
This is an experimental science. Techniques or guidelines have been defined to record a
number of external signs of humans while executing a task or solving a problem.
Sometimes the experimental subjects are requested to think aloud. These observable traces,
called protocols, are used to evaluate theories of cognition or to formulate new theories.
Various theories of cognition have been proposed according to various possible levels of
conceptualization that can be adopted to interpret the data. The earliest ones were inspired
from the neuron model of Pitts and McCulloch [Pitts & McCulloch, 1947]. Now this line
of research is known as parallel distributed processing [Rumelhart & Hinton, 1986]. This
level of analysis did inspire teaching/training approaches that have been violently rejected
by the public.
The first notable applications of Cognitive Science to ITS research are found one step
further in abstraction The basic elements of cognition are no longer the particular states of
neural synapses, enabling or not the firing of neurons, but production rules of the form:
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IF <<premises are true>> THEN <<consequences follow>>
These rules can vary in complexity and even admit variables; priority levels can be ascribed
to them when several rules can be applied (fired) simultaneously.
This is the essence of the ACT theory of cognition [Anderson, 1983]. According to this
theory, the goal of education is to get students to acquire a proper set of production rules.
Over the years the ACT theory has been refined to account for the acquisition of production
rules. A variety of mechanisms are conjectured to account for how students progressively
refine and transform their initial declarative statements concerning a knowledge item into a
precise and efficient production rule.
The notion of a declarative statement in the context of psychological research has never
been very clear. It stands for the imperfect expression of a knowledge item in terms already
acquired rather general concepts. It may be obscured by omissions and errors. The ACT
theory also explains how a sequence of productions can be compiled into a single
production, possibly of a higher conceptual level.
The ACT theory has been tested in ITS systems that are among the few ITS systems that
have made it successfully out of the research laboratory. However it does not seem that
these systems were built in strict accordance with the theory. For instance, both in the
Geometry Tutor [Anderson, Boyle & Yost, 1985] and the LISP Tutor [Anderson, 1989] it is
hard to see any evidence of students refining declarative statements in order to obtain
production rules. Instead, the student acts on the premises and goals of a problem by
successive application of menu-selected productions in order to obtain a valid path from
premises to conclusions (goals).
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The next step in an elaboration of a Cognitive Science has been to consider more complex
structures than production rules as the basis of cognition. The idea is to chunk together all
the elements that characterize a recurrent situation.
Roger Schank has called these
structures "scripts" [Schank, 1991]. For example in the context of a restaurant script the
bill refers to some piece of paper with some number written on it and implying a transfer of
money; it is not the mouth appendage of some aquatic bird. In order to account for the
acquisition of scripts and to cope with the combinatorial explosion in the number of scripts
a normal human could store in his head, Schank has hypothesized a limited number of
operators to create, transform scripts and carry operations accounting for human problem
solving abilities. It took a long time for these more general knowledge structures, called
scripts, frames and object hierarchies, cases, to be included into ITS [Schank, 1991].
Unfortunately these systems have not yet attained a level of development that allow testing
with genuine students.
In spite of the heightened expectations they created the major Cognitive Science models for
human learning (and memory management) have not succeeded in imparting a unified
outlook to the field of Cognitive Science. Instead there is a great diversity of work strongly
influenced by the major psychological schools of thought. An evolution toward more
tollerance for introspection, so long as it is backed-up with objective observations is
perceptible; this is probably a result of the failure to convincingly validate the major
models.
One Cognitive Science concept that is most interesting for ITS research is that of a student
model, inspired from the works of Johnson-Laird on mental models [Johnson-Laird, 1983].
A mental model is considered as a data structure that can be used to determine what the
student engaged in an ITS session has already seen, the exercises he has mastered, his
misconceptions (bugs) as well as a number of personal psychological characteristics. The
15
main problem in implementing non trivial student models in ITS has been of getting
information that is sufficiently precise and exact to be useful for guiding a tutoring session.
Some researchers [Self, 1990] consider developing an appropriate student model as
intractable.
In the face of these difficulties ITS designers have turned to educational theorists [Gagne,
1984] and attempted to exploit their typologies of student abilities and learning levels
taking into account the context in which a knowledge has been acquired [Frasson & de La
Passardiere, 1990]. However, if this approach is more precise and gives a way to predict
the results of learning, it is still to be validated in various tutorials strategies.
Instructional
design
remains
a
highly complex
task
(ill-structured),
involving
decomposition of knowledge into sub-units, structuration of a problem into tasks and subtasks, contacts with the prior accumulated experience of the field, choice among solutions
and adaptation to the problem, use of iterative operations,...[Pirolli & Russell, 1990].
Several tools could be developed to facilitate the design process and provide a productive
environment.
Other researchers have been more pragmatic in proposing ways of managing tutorial
discourse, i.e. the dialog between the student and the ITS. These systems have concerned
very specific task domains such as Algebra and Statics [Woolf, 1987]. More theoretical
considerations on managing tutorial discourse are available in [Woolf & Murray, 1987,
Baker, 1990].
As things stands at present, the goal of achieving an "ITS shell" has not been attained. This
means that the ITS architecture cannot be decided upon independently of the subject
domain.
Currently, building an ITS for a non trivial domain knowledge still requires a major effort.
This may explain in part why there are relatively few ITS effectively in use at present.
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4 ITS research as a pragmatic, interdisciplinary research domain.
Two tendencies are emerging in ITS development. First, traditional ITS is changing due to
slowness of development and disappointments in implementing generic ITS systems.
Secondly, other computer-based methodologies prooved useful.
A number of ITS researchers have changed their perspectives and have adopted a more
pragmatic view of how to produce effective systems to help educate or train students. On
the other hand they have realized that a number of practical developments in Information
Retrieval, Office Automation Systems, Hypertext, Graphical User Interfaces and Computer
Simulation languages could have a significant impact on the way people learn.
Some former ITS researchers now concentrate their efforts on building Interactive
Learning Environments (ILE). These environments allow students to experiment with
powerful computer tools to access information and incorporate that information in
structures of their own design. For instance, with the system Cabri Geometre [Allen & al,
1992], students build geometric structures on their computer screens with specifications, for
instance, that two segments are of equal length. Then, students can modify characterisitics
of their figures and still have some freedom to make general observations on the
transformations.
The idea underlying ILE research is that the goals and the control of the student interactions
with the computer system are now mainly external to the computer system. This is left to
17
the responsibility of a human teacher working with the students or to that of the students
themselves working in groups.
This is a radical departure from the ITS research goals of guiding one student through a
fixed curriculum. In the mean time it is realized that ILEs and ITSs are just extreme cases
of computer based systems for teaching and learning and that many interesting and valuable
developments can be made in between these two extremes. In fact the positions of ILE and
ITS researchers have never been very strict on that issue of control in learning/tutoring
sessions. ITS researchers have advocated coaching (or over the shoulder) control strategies.
Systems implementing these strategies let the student perform tasks in a tutorial session and
intervene only when student actions denote a major departure from the goals. Then
explanations are provided and the student is set on a path lined with remedial actions. A
more recent concept is that of a expert critiquing systems [Silverman, 1992] that comments
on the student solutions once they have been obtained. Critiquing systems are situated
between coaching and exploratory learning and are computer systems that help prevent and
reduce judgment errors and biases in experts. They shoud cue their user toward the correct
solution.
On the other hand the proponents of ILE, advocate the use powerful tools to constrain
novice users of a learning environments on paths that are guaranteed to result in a valuable
learning experience. Then as the student gains expertise, these helps are progressively
removed. This strategy of temporary constraints or aids to learning is called "scaffolding"
[Soloway, 1992, Merill & al, 1992]. Also, this avoids the big gap between schools and
workplace and lets the students learning by doing in an active, constructive process. A
better understanding of this approach (learning by doing) is needed, aiming at identifying
higher order thinking skills, learning and metacognitive skills.
ILE facilitate individual and collaborative works, strengthened by using multimedia
facilities, and give new responsabilities to the teachers. Experiences shared with peers
18
[Chan & al, 1988] allow to stimulate learners and enhance the learning process by
observing the reactions of other students, comparing the tasks and doing.
SHERLOCK [Lajoie & Lesgold, 1989] is another example of a job-situated training. It was
developed for training technicians on repairing electronic navigation equipment from F-15
aircraft, using a test station to isolate the source of failures. So, the practice environment, in
SHERLOCK, is a realistic computer simulation of a job environment, using control and
display panels of a test station, with knobs and dials. An important aspect is the
combination of intelligent coaching with realistic simulation of the actual work
environment. This environment can quickly show trainees sequences of fault diagnosis
tasks selected for instructional purposes. It can provide instruction when the trainees need it
and can tailor the help provided, providing the set of available experiences or focussing on
the problem solving to be learned and giving appropriate levels of support. Figure 2 shows
the test station with its twelve drawers, the specific navigation unit of the plane and the test
package which interconnects the station with the unit. Each of the drawer can be expanded
(by pointing and clicking on it) showing new front displays, control panels, circuit
diagrams, and indicating on the diagrams where the measurement instruments should be
placed for tests.
(insert figure 2 here)
Figure 2 The SHERLOCK environment
SHERLOCK is an example of a system that makes good use of visual representations, of
simulations and of flexible guidance through a curriculum. Visual representations have
19
been found to help student structure and manipulate complex reasonings [Kaltenbach, &
Frasson 1989], [Merill & al, 1992]. Experiences done with GIL (Graphical Instruction in
LISP), from Merril & al, have shown that student master programming more quickly and
with less difficulty than student using a standard programming environment. We conjecture
that it is this aspect of the LISP and the Geometry tutors that accounts principally for the
recorded success of these systems.
A related element that concurs to the practical success of ITSs is the possibility of getting
simultaneous views of the subject domain. For instance in RAPIDS , [Soloway, 1992], the
trainee manipulates components of complex helicopter engine on a schematic view and sees
the results of his actions on a realistic video of the real engine as well as on the schematic
representation. A step further in this direction is to present simultaneous views of a device
and of the student plans to diagnose a fault on the device, as illustrated in the PIF system
[Frasson & al, 1992]. Three worlds of manipulation are provided: P (physical) in which the
physical aspect of the device is shown, F (functional) where the corresponding parts of the
device can be examined in a diagram and I (intentional) in which the trainee can elaborate
plan to find the origin of a faulty unit.
(insert figure 3 here)
Figure 3 The PIF system
Such a setting blurs the distinction between ILE and ITS since with it, it is possible to
provide a range of tutorial control between no control (the student plan is not validated by
the system) and full control (the system assists the student in making plans and criticizes the
steps the student makes).
20
Conclusion
General Cognitive Theories are currently not sufficient to determine what is the best
architecture for ITS. Current ITS research is mostly experimental. The fine tuning to be
done can be acomplished by letting students experiment with ITS allowing a large number
of design options, and powerful knowledge construction tools. The idea is that student
together with their instructors and the help of computer tools, construct knowledge that can
be exploited (used) by others.
There exists a real need for developing gradation of facilities, guidance system, learning
platforms, flexibility that allows to accomodate a great variety of users (independant or
guided). Using ILE for constructing ITS has a triple advantage:
- More advanced students learn more and better by actively constructing knowledge.
- The resulting knowledge is in a form that is better adapted for novice in a domain.
- It offers an economical way to produce and generalize the use of ITS.
The main comments and recommendations that result from the talks given by several
invited speakers at the ITS-92 conference are the following:
- We (researchers in ITS) have tried to incorporate too large systems devoted to complex
aspects of human behavior. We must be realist and focus the activities on what seems
efficient in term of training.
- ITS draws on more fine-grained psychological theories of learning.
- New technology provides more flexible response to student. Powerful computers are
coming soon but the order of magnitude of difficulties, in terms of AI development, remain
high.
21
- Intelligent simulations of critical parts of a work environment and intelligent help of
trainees in problem solving situation allow to acquire very quickly on-the-job experience.
- ITS research wants to go practical due to the high demand of industry and economy but
development methodologies should be considered too. The problem, pushed by the growth
of technology, is that this methodology have chance to be developed later.
After several years of multidiciplinary research the orientations for ITS development are
going more practical and based on efficient strategies. However, fundamental research will
still be necessary, involving more and more the disciplines in question.
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