Title: Beverly Park Woolf
1Introduction to Intelligent Tutoring Systems
- Beverly Park Woolf
- University of Massachusetts/Amherst
- U.S.A
- Bev_at_cs.umass.edu
2AGENDA
- Introduction
- Features of Intelligent Tutors
- Two Example Tutors
- Three Disciplines
- Components of Intelligent Tutors
3Main Drivers for a Change in Education
- Artificial intelligence (AI) which has led to a
deeper understanding of how to represent
knowledge, especially how to knowledge, such as
procedural knowledge and reasoning about
knowledge - Cognitive science has led to a deeper
understanding of how people think, solve problems
and learn and - The Web provides an unlimited source of
information, available anytime and anyplace.
4- Internet provides a location--but not an education
5Issues addressed by this research
- What is the nature of knowledge?
- How is knowledge represented?
- How can an individual student be helped to learn?
- What styles of teaching interactions are
effective and when? - What misconceptions do learners have?
6Intelligent Tutors Do Improve Learning
- Intelligent tutors
- produce the same improvement as one-on-one human
tutoring and effectively reduce by one-third to
one-half the time required for learning Regian,
1997. (One-on-one tutoring increases performance
to around 98 in a standard classroom Bloom,
1984). - increase effectiveness by 30 as compared to
traditional instruction Fletcher, 199 Region,
1997 - Networked versions reduce the need for training
support personnel by about 70 and operating
costs by about 92. - One-on-one tutoring increases performance to
around the 98 in a standard classroom Bloom,
1984.
7Traditional Education Technology
- Is frame-based or directed each page, every
instructor response and every sequence or path of
topics is predefined by the author and presented
in a lock-step fashion. - Assumes that an instructional designer can
specify the correct learning sequence for all
students, months before a student interacts with
the software.
8Features of Intelligent Tutors
- Generativity
- Student modeling
- Expert modeling
- Instructional modeling
- Self-Improving
No agreement exists on which features are
necessary to define an intelligent tutor. Many
computer aided instructional systems contain one
or more of the features listed above. Teaching
systems lie along a continuum that runs from
simple frame-based systems to very sophisticated
and intelligent tutoring. The most sophisticated
systems include, to varying degrees, these
features.
9Features of Intelligent Tutors
- Generativity --(i.e., generate appropriate
problems, hints and help, customized to student
learning needs.) - Student modeling-- (i.e. assess the current state
of the students knowledge and learning needs and
do something instructionally useful on the basis
of this assessment) - Expert modeling-- (i. e. assess and model expert
performance in the domain and to do something
instructionally useful on the basis of this
assessment) - Instructional modeling--(i.e., change the
teaching mode based on inferences about the
students learning). - Self-Improving-- (i.e., ability to monitor,
evaluate and improve its own teaching performance
as a result of experience.)
10Assumptions of Intelligent Tutors
- Intelligent reasoning can be included in
educational software (e.g., simulations, games or
instruction) to support both teachers and
student - Student thinking processes can be
- modeled and tracked
- Student actions can be predicted, understood and
remediated - Teacher knowledge can be codified and carefully
presented to a students
11AnimalWatch, Example Tutor
Example of a simple addition problem in
AnimalWatch
AnimalWatch provided effective,
confidence-enhancing arithmetic instruction for
elementary students.
12AnimalWatch
Example hint on a simple multiplication problem
In contrast to common drill-and-practice
systems, AnimalWatch modified its responses to
conform to the students learning styles. The
tutor presented problems that required
increasingly challenging application of the
cognitive subtasks involved in solving the
problems (e.g. adding fractions with like
denominators, adding fractions with different
denominators, etc.).
13Cardiac Tutor
The Simulated Patient. The intravenous line has
been installed (IV in), chest compressions are
in progress, ventilation has not yet begun and
the electronic shock system is discharged. The
icons on the chest and near the face indicate
that compressions are in progress and ventilation
is not being used.
14Cardiac Tutor
- The Cardiac Tutor was generative because each
case or patient situation was dynamically
altered, in the middle of the case, to provide
the particular arrythmia that a student needed to
experience. -
- The tutor had a complex domain model represented
rules of each arrythmias and the required
therapy. Nodes represented states of cardiac
arrest or arrythmias and arcs represented the
probability that a the simulated patient would
move to a new physiological state following a
specified treatment. -
- The student model tracked student responses to
each arrythmia. Student action was connected to
the original simulation state so the student
could request additional information about past
actions.
15AnimalWatch
- AnimalWatch was generative since all math
problems, hints and help were generated on the
fly based on student learning needs observed by
the tutor. - The tutor modeled expert knowledge of arithmetic
as a topic network with nodes such as subtract
fractions'' or multiply whole numbers. - Student modeling dynamically recorded each
sub-task learned or needed based on student
action. - The tutor was self-improving in that it used
machine-learning techniques to predict how long a
student needed to solve a problem and each
students proficiency.
16This Research AreaEncompasses Several Disciplines
17Tools and Methods arederived from
- Artificial Intelligence
- Design and build systems that exhibit
intelligence - Cognitive Science
- Investigates how intelligent entities (human or
computer) interact with their environment, and
acquire - Education
- Explore effective methods of supporting teaching
and learning
18New Disciplines Have Formed
19Components of an Intelligent Tutor
20Fractions
Represent Domain Knowledge
21Cardiac Resuscitation
Represent Domain Knowledge