Transcript Slide 1
Connecting Curriculum to
Problem-Solving Through
Distributed Intelligent Tutoring
Sharon J. Derry
Dept. of Educational Psychology
Wisconsin Center for Education
Research
University of Wisconsin-Madison
Contributors
Mary
Leonard
Donald Wortham
Alan Hackbarth
Margaret Wilsman
Michael Peterson
In Nintendo's "The Legend of Zelda™," Link
must travel through many rooms arranged in
a coordinate maze to find the silver arrow.
From the entry room, he goes right 3 rooms,
up 4 rooms, left 5 rooms, down 2 rooms, left
1 room, up 3 rooms, and right 4 rooms to find
the chamber that has the silver arrow. If the
entry room has coordinates (0,0) and all
directions are as you view the maze, find the
coordinates of the room with the arrow.
Making School Curricula Useful
and Interesting to Students
Curricular domains
– Statistics, Algebra, Earth Systems Science
Cognitive Theoretical Approach
– “Authentic” problem contexts for learning
– Relevant, knowledge-appropriate problems
Instructional strategies allowing choices in
problem framing and selection
Projects
TiPS Intelligent Tutor
– Adult basic skills math
Situated Simulations for Statistics
– Middle school math, sci, social studies
– Pre-service teachers
“The Learning Sciences” through
Instructional Design
– Pre-service teachers
Studies of Inquiry Science Classrooms
– Middle schools
Results/Findings
Moderate to Good Learning Gains
Desired Performance Improvements Over
Comparison Classrooms
Resource Intensive
Mixed Reviews
Conclusion:
– Teaching and learning curriculum in the
context of authentic problem solving requiring
self-directed problem framing and learning
presents tough instructional design challenges.
The Trojan Horse
Challenges
I. Problem Finding
II. Domain “Disorganization”
III. The Modeling Problem
Design Challenges in ITS Terms
Modeling Domain Knowledge
Modeling Students’ Domain Knowledge
Problem Modeling With Domain Concepts
Real-Time Modeling of Students’ Evolving
Solutions
Modeling Students’ Affective States
Scaffolding Student Problem Solving
Selecting Tutorial Digressions
Machine Learning
I. Problem Finding
1. No constraints on students’
problem choice.
2. Full constraints on students’
problem choice.
3. Guided/mentored problem
framing.
Full Constraints on Choice
Examples:
– Math problems matching goals are assigned.
Advantages:
–
–
–
–
Problems match student needs, curriculum.
It's expected, accepted, desired.
Arguably appropriate for early learning.
Does not “reorganize“ domain.
Challenges:
– Students do not learn to find/frame problems.
– Problems do not arise from interests.
– Problems often contrived, trivial.
Guided Problem Finding
Examples
– Practice-based professional development
– Project-based learning
Advantages
– Learners find personally relevant problems
Dilemmas
– Finding curriculum-appropriate problem
without prior knowledge of curriculum?
– How model problem framing?
– Important problems “disorganize” curriculum.
II. The Domain “Disorganization
Problem”
LEARNING SCIENCES
COGNITIVE
THEORY
PROB
1
PROB
2
PROB
3
IP
VIEW
SOCIO
COGNI
TIVE
X-THEORY
IDEAS
SOCIO
CULTURAL
THEORY
Challenges
I. Problem Finding
II. Domain “Disorganization”
III. The Modeling Problem
The Modeling Problem: A Story
The
authentic context: Building
balloon Cars (LBD™)
A curriculum goal: Newton’s Third
Law
Summary of Teacher’s Modeling
Makes tutorial digression
Models operation of car with abstract
physics concepts
– Develops intermediate representation making
tradeoffs on what “not” to model
Exhibits in-depth knowledge of physics
Engages in critical reflective practice
regarding representational system and
success of her lecture
ITS’s Intelligent Learning
Communities
New
21st Century Goals for Learners:
– Participate in interdisciplinary learning
communities that use subject
knowledge to frame and solve real
problems.
– Acquire mindsets and skills for lifelong
learning, including ability to use human
and technology resources to acquire
knowledge during problem solving.
– Develop concern for real-world needs
and willingness to become engaged
citizens.
Hands-On Environmentalism
Community
Standards
Evolving
Case
Library
ESS Project
Case Templates
(epistemological
commitments)
Continuing
Community:
sets
Collaborative
Tools
Project
Collaborations
Scientists,
Teachers,
Facilitators,
Env. Groups,
K-12 Students
seed
ESS Web
Resources
Enrolled
Learners
Everything is vague to a degree
you do not realize till you have
tried to make it precise.
Bertrand Russell