INTRODUCTION
Education is
currently undergoing a major transition in modern societies. Many authorities
attribute this transition to serious limitations in the traditional lecturing
approach to education, which places the student in a passive role.
Recently, a
wave of innovations, which is stimulated by the revolution in the Information
Technology, promises to revitalize schools and the education system.
GENERATIVE CAI
In the 1960's, researchers created a number of
Computer Assisted Instructional (CAI) systems that were generative (Uhr, 1969). These programs generated sets of
problems designed to enhance student performance in skill-based domains,
primarily arithmetic and vocabulary recall. Essentially, these were automated
flash card systems, designed to present the student with a problem, receive and
record the student's response, and tabulate the student's overall performance
on the task
THE STRUCTURE OF AN ITS SYSTEM
Intelligent tutoring systems consist of
four different subsystems or modules: the interface module, the expert module,
the student module, and the tutor module. The interface module provides the means for the student to interact
with the ITS, usually through a graphical user interface and sometimes
through a rich simulation of the task domain the student is learning
(e.g., controlling a power plant or performing a medical operation). The expert module references an expert or
domain model containing a description of the knowledge or behaviors that
represent expertise in the subject-matter domain the ITS is teaching, often an expert
system or cognitive model. An example would be the kind of
diagnostic and subsequent corrective actions an expert technician takes when
confronted with a malfunctioning thermostat. The student module uses a student model containing descriptions of
student knowledge or behaviors, including his misconceptions and knowledge
gaps. An apprentice technician might, for instance, believe a thermostat also
signals too high temperatures to a furnace (misconception) or might not know
about thermostats that also gauge the outdoor temperature (knowledge gap). A
mismatch between a student's behavior or knowledge and the expert's presumed
behavior or knowledge is signaled to the tutor
module, which subsequently takes corrective action, such as providing
feedback or remedial instruction. To be able to do this, it needs information
about what a human tutor in such situations would do: the tutor model.
An intelligent tutoring system
is only as effective as the various models it relies on to adequately model
expert, student and tutor knowledge and behavior. Thus, building an ITS needs
careful preparation in terms of describing the knowledge and possible behaviors
of experts, students and tutors. This description needs to be done in a formal
language in order that the ITS may process the information and draw
inferences in order to generate feedback or instruction. Therefore a mere
description is not enough; the knowledge contained in the models should be
organized and linked to an inference
engine. It is through the latter's interaction with the descriptive data
that tutorial feedback is generated.
USE IN
PRACTICE
All this is a substantial amount of work,
even if authoring tools have become available to ease the
task. This means that building an ITS is an option only in situations in which
they, in spite of their relatively high development costs, still reduce the
overall costs through reducing the need for human instructors or sufficiently
boosting overall productivity. Such situations occur when large groups need to
be tutored simultaneously or many replicated tutoring efforts are needed. Cases
in point are technical training situations such as training of military
recruits and high school mathematics. One specific type of intelligent tutoring
system, cognitive tutors, has been incorporated into mathematics
curricula in a substantial number of United States
By the mid-1980's, much of enthusiasm in AI for creating
"thinking" computers had waned as the field began to mature.
Researchers turned to the more prosaic tasks of building expert systems that
could function well in constrained domains, such as troubleshooting and
diagnostic systems. At the same time, as ITS began to move out of the AI
laboratories into classrooms and other instructional settings, they began to
attract critical reactions. Some shortcomings of ITS became apparent as
researchers realized that the problems associated with creating ITS were more
intractable than they had originally anticipated. Rosenberg
The systems are not grounded in a substantiated model of learning.
Model formulation should be preceded by protocol analysis, but very little
analysis is done, almost none of it qualitative. ITS models should be validated
by the teachers and students who will use the systems, but ITS researchers do
not appear to consult these experts.
Testing is incomplete, inconclusive, or in some cases totally
lacking. Data on computerized tutorials are, at best, mixed. The almost
universally positive claims for ITS and other computerized instructional
systems, most notable in the education literature, are based on results from
severely flawed tests.
It was obvious that the basic premises of ITS research needed
revision.
Intelligent tutoring systems, four different subsystems
or modules:
1. The interface module: It provides the means for the student to
interact with the ITS, usually through a graphical user
interface and sometimes through a rich simulation of the task domain the student is
learning (e.g., controlling a power plant or performing a medical operation).
2. The expert module: It references an expert or domain model
containing a description of the knowledge or behaviors that represent expertise
in the subject-matter domain the ITS is teaching, often an expert system or cognitive model. An example would be the kind of
diagnostic and subsequent corrective actions an expert technician takes when
confronted with a malfunctioning thermostat.
3.The student module: It uses a student model containing descriptions
of student knowledge or behaviors, including his misconceptions and knowledge
gaps. An apprentice technician might, for instance, believe a thermostat also
signals too high temperatures to a furnace (misconception) or might not know
about thermostats that also gauge the outdoor temperature (knowledge gap)
4. The tutor module: A mismatch between a student's behavior or
knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently
takes corrective action, such as providing feedback or remedial instruction. To
be able to do this, it needs information about what a human tutor in such
situations would do: the tutor model.
THE INTELLIGENT TUTORING SYSTEMS CONFERENCE
Ø in Montréal (Canada
Ø in San Antonio
Ø in Biarritz
(France ) and San Sebastian (Spain
Ø in Maceio (Brazil) by Rosa
Maria Vicari and Fábio Paraguaçu in 2004;
Ø in Jhongli (Taiwan
Ø The conference was recently
back in Montreal
Ø ITS'2010 will be held in
Pittsburgh (US) by Jack Mostow, Judy Kay, and Vincent Aleven.
SOME INTELLIGENT TUTORING SYSTEMS ARE
Ø Using technology for learning &
teaching science. (as
astronomy, space research, physics, mathematics and the earth sciences. )
PROSPECTS FOR CREATING AN INTELLIGENT TUTORING
SYSTEM
Ø Modularize the curriculum.
Ø Customize it for different student populations.
Ø Individualize the presentation and assessment of
the content.
Ø Collect data which instructors could use to
tutor and remediate students.
The first step in this process is to understand what others
had done before us and the implications for future developments.
In 1998, chemist Benny Johnson
founded Quantum Simulations, Inc. with high school mentor Dale Holder and
colleague Rebecca Renshaw to create highly interactive tutoring software for
the sciences.
Student modeling remains at the core of ITS
research (Holt, Dubs, Jones & Greer, 1994). What distinguishes ITS from CAI
is the goal of being able to respond to the individual student's learning style
to deliver customized instruction.
DIAGNOSIS
Diagnosis means that an ITS infers information about the
learner's state on three levels.
At the behavioral level,
ignoring the learner's knowledge and focusing only on the observable behavior.
At the epistemic level,
dealing with the learner's knowledge state and attempting to infer that state
based on observed behavior.
At the individual level,
covering such areas as the learner's personality, motivational style,
self-concept in relation to the domain in question, and conceptions the learner
has of the ITS. Wenger notes that, up to now, ITS have not been concerned with
the individual level. However, he advocates further research in this area as a
prerequisite for viewing the student as an active learner, rather than as a
passive recipient of knowledge Conclusion
CONCLUSION
Intelligent Tutoring Systems emerged from
Artificial Intelligence at the very time that AI was struggling to transcend
the goal of mimicking human intelligence by creating machines that could
"think" like humans. As researchers came to grips with the
intractable problems of this task, they realized that trying to emulate human
cognition with computers was misguided because they assumed that people thought
like computers. The resulting crisis provoked a reassessment of AI's goals,
allowing researchers to begin making progress in areas such as expert systems.
Expert systems research was productive because it concentrated on systems that
were useful in their right, rather than attempting to create
"thinking" machines. However, this shift in focus prompted many to
lose interest in ITS.
At the same time, educational psychology was
undergoing a paradigm shift from behaviorism towards cognition, constructivism,
and socially situated learning. This revolution prompted many educators to
question the practices that evolved during the post-war education boom. ITS
technology, much of which was grounded in the behaviorism of CAI, lost favor.
It might appear that ITS are doomed to become a footnote in the history
of both computer science and educational psychology. However, the prospect of
applying the rapidly expanding power of computers not just to information
management, but to knowledge communication, is too appealing to allow us to
dismiss ITS research just yet. Combining Wenger's framework with a global
perspective such as that suggested by Donald provides one possible avenue for
developing the necessary interdisciplinary theories upon which new research and
ITS can be developed. Moving towards a cognitive understanding of productive
communication environments is likely to be fruitful for both ITS and
educational researchers. In this way we may be able to create the theories and
technology required to make the dream of intelligent knowledge communication systems
a reality.
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