Beverly Park Woolf
University of Massachusetts/Amherst
U.S.A
Bev@cs.umass.edu
Introduction
Features of Intelligent Tutors
Two Example Tutors
Three Disciplines
Components of Intelligent Tutors
Main 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.
• Internet provides a location--but not an
education
Issues 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?
Intelligent 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].
Traditional 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.
Features 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.
Features 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 student’s 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 student’s learning).
 Self-Improving-- (i.e., ability to monitor, evaluate and improve its own teaching
performance as a result of experience.)
Assumptions 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
AnimalWatch, Example Tutor
Example of a simple addition problem in AnimalWatch
AnimalWatch provided effective, confidence-enhancing arithmetic instruction
for elementary students.
AnimalWatch
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.).
Cardiac Tutor
0 :3 9
Simul ati on
Medic al Reco rd
Chron ic Welln ess:
No Signi fic ant Risk
IV In
Disch ar ge
Comp res sing
Intu ba ting
Worse
Not Ventil ati ng
47 / 5
Bet te r
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.
Cardiac 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.
AnimalWatch
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
student’s proficiency.
This Research Area
Encompasses Several Disciplines
Tools and Methods are
derived 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
New Disciplines
Have Formed
Represent Domain Knowledge
Fractions
Fractions
Fraction Readiness
Addition /Subtraction
Similar Denominators
Multiplication/Division
Dissimilar Denominators
Find LCM
Multiply All fractions
Represent Domain Knowledge
Cardiac Resuscitation
•vtach
•vtach
•vfib
•asys
•brady
•vfib
•25%
•asys
•brady
•GOAL
•sinus