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Beverly Park Woolf

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Title: No Slide Title Author: CSCF Last modified by: CSCF Created Date: 12/1/2003 1:33:10 AM Document presentation format: On-screen Show Company: UMass - Amherst – PowerPoint PPT presentation

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Title: Beverly Park Woolf


1
Introduction to Intelligent Tutoring Systems
  • Beverly Park Woolf
  • University of Massachusetts/Amherst
  • U.S.A
  • Bev_at_cs.umass.edu

2
AGENDA
  • Introduction
  • Features of Intelligent Tutors
  • Two Example Tutors
  • Three Disciplines
  • Components of Intelligent Tutors

3
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.

4
  • Internet provides a location--but not an education

5
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?

6
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.

7
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.

8
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.
9
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 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.)

10
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

11
AnimalWatch, Example Tutor
Example of a simple addition problem in
AnimalWatch
AnimalWatch provided effective,
confidence-enhancing arithmetic instruction for
elementary students.
12
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.).
13
Cardiac 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.
14
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.

15
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
    students proficiency.

16
This Research AreaEncompasses Several Disciplines
17
Tools 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

18
New Disciplines Have Formed
19
Components of an Intelligent Tutor
20
Fractions
Represent Domain Knowledge
21
Cardiac Resuscitation
Represent Domain Knowledge
  • vtach
  • vtach
  • 60 in 10 sec
  • 10 in 10 sec
  • 30
  • 25
  • asys
  • asys
  • vfib
  • vfib
  • GOAL
  • 10
  • brady
  • sinus
  • 65
  • brady
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