Transcript This is the 5th Annual PSLC Summer School • 9th overall • Goals:

This is the 5th Annual PSLC Summer
School
• 9th overall
– ITS was focus in
2001 to 2004
• Goals:
– Learning science &
technology
concepts
– Hands-on project
you present on Fri
1
Studying and achieving robust
learning with PSLC resources
Ken Koedinger
HCI & Psychology
CMU Director of PSLC
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Vision for PSLC
• Why?
“rigorous, sustained scientific
research in education” (NRC, 2002)
Chasm between science & practice
Indicators: Ed achievement gaps persist,
Low hit rate of randomized controlled trials (<.2!)
• Underlying problem:
Many ideas,
too little sound scientific foundation
• Need: Basic research studies in the field
=> PSLC Purpose: Identify the conditions that
cause robust student learning
– Field-based rigorous science
– Leverage cognitive & computational theory, educational
technologies
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The Setting & Inspiration
• Rich tradition of research on Learning and
Instruction at CMU & University of Pittsburgh
– Basic Cognitive Science from CS & Psych collab
– Learning in academic domains
• Science, math, literacy, history …
• Many studies, but not enough cross talk
– Theory inspired intelligent tutors:
• Andes physics tutor in college classrooms
• Cognitive Algebra Tutor in 2500+ US schools
• A key PSLC inspiration: Educational technology as
research platform to launch new learning science
4
Overview
Next
• Background
– Intelligent Tutoring Systems
– Cognitive Task Analysis
• PSLC Methods, Resources, & Theory
– In vivo experimentation
– LearnLab courses & enabling technologies
– Theoretical framework
• Summary & Future
5
PSLC is about much more
than Intelligent Tutors
But tutors & course
evaluations were a key
inspiration
Quick review …
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Past Success: Intelligent Tutors Bring
Learning Science
to Schools!
• Intelligent tutoring
systems
– Automated 1:1 tutor
– Artificial Intelligence
– Cognitive Psychology
• Andes: College Physics
Tutor
– Replaces homework
Students: model problems with
diagrams, graphs, equations
Tutor: feedback, help,
reflective dialog
• Algebra Cognitive Tutor
– Part of complete course
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b
Cognitive Tutor Approach
Research base
Cognitive
Psychology
Artificial
Intelligence
Cognitive
Tutor
Technology
Curriculum Content
Math Instructors
Math Educators
NCTM Standards
Cognitive Tutors
Algebra I
Equation
Solver
Geometry
Algebra II
Cognitive Tutor Technology
• Cognitive Model: A system that can solve problems in
the various ways students can
Strategy 1:
Strategy 2:
Misconception:
IF the goal is
THEN rewrite
IF the goal is
THEN rewrite
IF the goal is
THEN rewrite
to solve a(bx+c) = d
this as abx + ac = d
to solve a(bx+c) = d
this as bx + c = d/a
to solve a(bx+c) = d
this as abx + c = d
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Cognitive Tutor Technology
• Cognitive Model: A system that can solve problems in
the various ways students can
3(2x - 5) = 9
If goal is solve a(bx+c) = d
Then rewrite as abx + ac = d
If goal is solve a(bx+c) = d
Then rewrite as abx + c = d
If goal is solve a(bx+c) = d
Then rewrite as bx+c = d/a
6x - 15 = 9
2x - 5 = 3
6x - 5 = 9
• Model Tracing: Follows student through their individual
approach to a problem -> context-sensitive instruction
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Cognitive Tutor Technology
• Cognitive Model: A system that can solve problems in
the various ways students can
3(2x - 5) = 9
If goal is solve a(bx+c) = d
Then rewrite as abx + ac = d
If goal is solve a(bx+c) = d
Then rewrite as abx + c = d
Hint message: “Distribute a
across the parentheses.”
Known? = 85% chance
6x - 15 = 9
Bug message: “You need to
multiply c by a also.”
Known? = 45%
2x - 5 = 3
6x - 5 = 9
• Model Tracing: Follows student through their individual
approach to a problem -> context-sensitive instruction
• Knowledge Tracing: Assesses student's knowledge
growth -> individualized activity selection and pacing
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Cognitive Tutor Course
Development Process
1.
2.
3.
4.
Client & problem identification
Identify the target task & “interface”
Perform Cognitive Task Analysis (CTA)
Create Cognitive Model & Tutor
a. Enhance interface based on CTA
b. Create Cognitive Model based on CTA
c. Build a curriculum based on CTA
5. Pilot & Parametric Studies
6. Classroom Evaluation & Dissemination
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b
Cognitive Tutor Approach
Research base
Cognitive
Psychology
Artificial
Intelligence
Cognitive
Tutor
Technology
Curriculum Content
Math Instructors
Math Educators
NCTM Standards
Cognitive Tutors
Algebra I
Equation
Solver
Geometry
Algebra II
Difficulty Factors Assessment:
Discovering What is Hard for Students to Learn
Which problem type is most difficult for Algebra students?
Story Problem
As a waiter, Ted gets $6 per hour. One night he made $66 in
tips and earned a total of $81.90. How many hours did Ted
work?
Word Problem
Starting with some number, if I multiply it by 6 and then add
66, I get 81.90. What number did I start with?
Equation
x * 6 + 66 = 81.90
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Algebra Student Results:
Story Problems are Easier!
Percent Correct
80%
70%
61%
60%
42%
40%
20%
0%
Story
Word
Equation
Problem Representation
Koedinger, & Nathan, (2004). The real story behind story problems: Effects of representations
on quantitative reasoning. The Journal of the Learning Sciences.
Koedinger, Alibali, & Nathan (2008). Trade-offs between grounded and abstract representations:
Evidence from algebra problem solving. Cognitive Science.
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Expert Blind Spot:
Expertise can impair judgment of student
difficulties
100
90
80
% making
correct
ranking
(equations
hardest)
70
60
50
40
30
20
10
0
Elementary
Teachers
Middle
School
Teachers
High School
Teachers
Nathan , M. J. & Koedinge r, K. R. (2000). An inve stigation of teacher s' beliefs of
students' algebra deve lopment. Cognition and Instruction, 18(2), 207-235
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“The Student Is Not Like Me”
• To avoid your expert blindspot,
remember the mantra:
“The Student Is Not Like Me”
• Perform Cognitive Task Analysis
to find out what students are like
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Cognitive Tutor Course
Development Process
1.
2.
3.
4.
Client & problem identification
Identify the target task & “interface”
Perform Cognitive Task Analysis (CTA)
Create Cognitive Model & Tutor
a. Enhance interface based on CTA
b. Create Cognitive Model based on CTA
c. Build a curriculum based on CTA
5. Pilot & Parametric Studies
6. Classroom Evaluation & Dissemination
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Tutors make a significant difference
in improving student learning!
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• Andes: College Physics
Tutor
– Field studies: Significant
improvements in
student learning
• Algebra Cognitive Tutor
– 10+ full year field
studies: improvements
on problem solving,
concepts, basic skills
– Regularly used in 1000s
of schools by 100,000s
of students!!
70
65
Control
Andes
60
55
50
2000
60
2001
2002
2003
Traditional Algebra Course
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Cognitive Tutor Algebra
40
30
20
10
0
Iowa
SAT subset
Problem
Solving
Representations
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President Obama on
Intelligent Tutoring
Systems!
“[W]e will devote more than three percent of our GDP to
research and development. …. Just think what this
will allow us to accomplish: solar cells as cheap as
paint, and green buildings that produce all of the
energy they consume; learning software as effective as
a personal tutor; prosthetics so advanced that you
could play the piano again; an expansion of the
frontiers of human knowledge about ourselves and
world the around us. We can do this.”
http://my.barackobama.com/page/community/post/amy
hamblin/gGxW3n
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Prior achievement:
Intelligent Tutoring Systems
bring learning science to schools
A key PSLC inspiration:
Educational technology as
research platform to generate
new learning science
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Overview
• Background
– Intelligent Tutoring Systems
– Cognitive Task Analysis
Next
• PSLC Methods, Resources, & Theory
– In vivo experimentation
– LearnLab courses & enabling technologies
– Theoretical framework
• Summary & Future
22
PSLC Statement of Purpose
Leverage cognitive and
computational theory to
identify the instructional
conditions that cause
robust student learning.
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What is Robust Learning?
• Robust Learning is learning that
– transfers to novel tasks
– retained over the long term, and/or
– accelerates future learning
• Robust learning requires that students develop
both
– conceptual understanding & sense-making skills
– procedural fluency with basic foundational skills
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PSLC Statement of Purpose
Leverage cognitive and
computational theory to
identify the instructional
conditions that cause
robust student learning.
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In Vivo Experiments
Principle-testing laboratory
quality in real classrooms
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In Vivo Experimentation
What is tested?
Methodology
– Instructional solution vs.
causal principle
Instructional
solution
Lab
Methodology features:
• What is tested?
Causal
principle
Lab
experiments
• Where & who?
• How?
– Treatment only vs.
Treatment + control
• Generalizing conclusions:
– Ecological validity: What
instructional activities
work in real classrooms?
– Internal validity: What
causal mechanisms
explain & predict?
Classroom
– Lab vs. classroom
Design
research
&
field trials
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In Vivo Experimentation
What is tested?
Methodology
– Instructional solution vs.
causal principle
Instructional
solution
Lab
Methodology features:
• What is tested?
Causal
principle
Lab
experiments
• Where & who?
• How?
– Treatment only vs.
Treatment + control
• Generalizing conclusions:
– Ecological validity: What
instructional activities
work in real classrooms?
– Internal validity: What
causal mechanisms
explain & predict?
Classroom
– Lab vs. classroom
Design
research
&
field trials
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In Vivo Experimentation
What is tested?
Methodology
– Instructional solution vs.
causal principle
Instructional
solution
Lab
Methodology features:
• What is tested?
Causal
principle
Lab
experiments
• Where & who?
• How?
– Treatment only vs.
Treatment + control
• Generalizing conclusions:
– Ecological validity: What
instructional activities
work in real classrooms?
– Internal validity: What
causal mechanisms
explain & predict?
Classroom
– Lab vs. classroom
Design
research
&
field trials
In Vivo
learning
experiments
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LearnLab
A Facility for Principle-Testing
Experiments in Classrooms
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LearnLab courses at
K12 & College Sites
• 6+ cyber-enabled courses:
Chemistry, Physics,
Algebra, Geometry,
Chinese, English
• Data collection
– Students do home/lab work
on tutors, vlab, OLI, …
– Log data, questionnaires,
tests  DataShop
Researchers
Schools
Learn
Lab
Chemistry virtual lab
Physics intelligent tutor
REAP vocabolary tutor
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PSLC Enabling Technologies
• Tools for developing instruction & experiments
– CTAT (cognitive tutoring systems)
• SimStudent (generalizing an example-tracing tutor)
– OLI (learning management)
– TuTalk (natural language dialogue)
– REAP (authentic texts)
• Tools for data analysis
– DataShop
– TagHelper
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LearnLab Products
Infrastructure created and highly used
• LearnLab courses have supported over
150 in vivo experiments
• Established DataShop: A vast open data
repository & associated tools
– 110,000 student hours of data
• 21 million transactions at ~15 second intervals
– New data analysis & modeling algorithms
– 67 papers, >35 are secondary data analysis not
possible without DataShop
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PSLC Statement of Purpose
Leverage cognitive and
computational theory to
identify the instructional
conditions that cause
robust student learning.
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Typical Instructional Study
• Compare effects of 2 instructional conditions in lab
• Pre- & post-test similar to tasks in instruction
Instruction
Expert
Novice
Learning
Pre-test
Post-test
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PSLC Studies
• Macro: Measures of robust learning
• Micro analysis: knowledge, learning, interactions
• Studies run in vivo: social & motivational context
Social
context of
classroom
Instruction
Expert
Novice
Knowledge:
Shallow,
perceptual
Instructional Events
Learning
Knowledge:
Deep,
conceptual,
fluent
Assessment
Events
Pre-test
Post-test Long-term retention,
Post-test:
transfer, accelerated future learning
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Develop a research-based,
but practical framework
• Theoretical framework key goals
– Support reliable generalization from empirical
studies to guide design of effective ed practices
Two levels of theorizing:
• Macro level
– What instructional principles explain how changes in the
instructional environment cause changes in robust
learning?
• Micro level
– Can learning be explained in terms of what knowledge
components are acquired at individual learning events?
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Example study at macro level:
Hausmann & VanLehn 2007
• Research question
– Should instruction provide explanations and/or elicit
“self-explanations” from students?
• Study design
– All students see 3 examples & 3 problems
• Examples: Watch video of expert solving problem
• Problems: Solve in the Andes intelligent tutor
– Treatment variables:
• Videos include justifications for steps or do not
• Students are prompted to “self-explain” or paraphrase
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Paraphrase
Explan
Selfexplain
X
No
explan
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Paraphrase
Selfexplain
Explan
No
explan
X
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Self-explanations =>
greater robust learning
• Transfer to new electricity
homework problems
Paraphrase, Paraphrase, Self-explain, Self-explain,
With just. Without just. With just. Without just.
0
(hints+errors) / steps
• Justifications: no effect!
• Immediate test on
electricity problems:
Paraphrase, Paraphrase, Self-explain, Self-explain,
With just. Without just. With just. Without just.
0.2
0.4
0.90
0.98
0.67
0.67
0.45
0.4
0.6
0.8
1
1.2
• Instruction on electricity unit =>
accelerated future learning of
magnetism!
Paraphrase, Paraphrase, Self-explain, Self-explain,
With just. Without just. With just. Without just.
0
0.8
1.2
0.37
0.2
0.6
1
1.04
1.4
(hints+errors) / steps
(hints+errors) / steps
0
0.69
1.17
0.91
0.75
0.68
0.2
0.4
0.6
0.8
1
1.2
1.4
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Key features of H&V study
• In vivo experiment
– Ran live in 4 physics sections at US Naval
Academy
– Principle-focused: 2x2 single treatment
variations
– Tight control manipulated through
technology
• Use of Andes tutor
=> repeated embedded assessment without
disrupting course
• Data in DataShop (more later)
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Develop a research-based,
but practical framework
• Theoretical framework key goals
– Support reliable generalization from empirical
studies to guide design of effective ed practices
Two levels of theorizing:
• Macro level
– What instructional principles explain how changes in the
instructional environment cause changes in robust
learning?
• Micro level
– Can learning be explained in terms of what knowledge
components are acquired at individual learning events?
43
Knowledge Components
• Knowledge Component
– A mental structure or process that a learner uses,
alone or in combination with other knowledge
components, to accomplish steps in a task or a
problem-- PSLC Wiki
• Evidence that the Knowledge Component level
functions in learning …
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Back to H&V study: Micro-analysis
Learning curve for main KC
Self-explanation effect tapers but not to zero
7.00
(hints+errors)/steps
6.00
Instructional
explanation
5.00
4.00
3.00
2.00
Selfexplanation
1.00
0.00
Problem1
Example1
Problem2
Example2
Problem3
Example3
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PSLC wiki: Principles &
studies that support them
Instructional Principle pages unify across studies
Points to Hausmann’s study page
(and other studies too)
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PSLC wiki: Principles &
studies that support them
Hausmann’s study description:
With links to concepts in glossary
48
PSLC wiki: Principles &
studies that support them
Self-explanation glossary entry
~200 concepts in glossary
49
Research Highlights
• Synthesizing worked examples &
self-explanation research
– 10+ studies in multiple 4 math & science domains
– New theory: It’s not just cognitive load!
• Examples for deep feature construction,
problems & feedback for shallow feature elimination
This work inspired new question: Does self-explanation enhance
language learning? Experiments in progress …
• Computational modeling of student Learning
– Simulated learning benefits of examples/demonstrations vs.
problem solving (Masuda et al., 2008)
• Theory outcome: problem solving practice is an important source of
negative examples
• Engineering: “programming by tutoring” is more cost-effective than
“programming by demonstration”
– Shallow vs. deep prior knowledge changes learning rate (Matsuda
et al., in press)
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Research Highlights (cont)
 Computational modeling of instructional assistance
 Assistance formula: Optimal learning (L) depends on
L
right level of assistance
L = P*Sb+(1-P)Fb
P*Sc+(1-P)Fc
Assistance
 Relevant to multiple experimental paradigms & dimensions of
instructional assistance
P
 Direct instruction (worked examples) vs. constructivism (testing effect)
Kirschner, Sweller, & Clark (2006). Why minimal guidance during instruction does not
work: An analysis of the failure of constructivist, discovery, problem-based,
experiential, and inquiry-based teaching. Educational Psychologist
 Concrete manipulatives vs. simple abstractions
Kaminski, Sloutsky, & Heckler (2008). The advantage of learning abstract examples
in learning math. Science.
 Formula provides path to resolve hot debates
51
Research Highlights (cont)
 Synthesis paper on computer tutoring of metacognition
Koedinger, Aleven, Roll, & Baker. (in press). In vivo experiments on whether
supporting metacognition in intelligent tutoring systems yields robust learning.
In Handbook of Metacognition in Education.
 Generalizes results across 7 studies, 3 domains, 4 populations
 Posed new questions about role of motivation
 Lasting effects of metacognitive support
 Computer-based tutoring of self-regulatory learning
 Technologically possible & can have a lasting effect
 Students who used help-seeking tutor demonstrated better
learning skills in later units after support was faded
 Spent 50% more time reading help messages
 Data mining for factors that affect student motivation
 Machine learning to analyze vast student interaction data
from full year math courses (Baker et al., in press a & b)
 Students more engaged on “rich” story problems than standard
 Surprise: Also more engaged on abstract equation exercises!
52
Overview
• Background
– Intelligent Tutoring Systems
– Cognitive Task Analysis
• PSLC Methods, Resources, & Theory
– In vivo experimentation
– LearnLab courses & enabling technologies
– Theoretical framework
• Summary & Future
Next
53
Summary
“rigorous, sustained scientific
research in education” (NRC, 2002)
• Why? Chasm between science & practice
• PSLC Purpose: Identify the conditions that cause
robust student learning
– Field-based rigorous science
– Leverage cognitive & computational theory, educational
technologies
• Results:
Sound evidence & deeper theory behind
principles to bridge chasm
• Impact:
spread use
Principles, methods, tools, & data in wide-
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Thrusts investigate
overlapping factors
Social
context of
classroom
Novice
Knowledge
Knowledge:
Shallow,
perceptual
Metacognition
Motivation
Instruction
Teacher
Interaction
Motivation
Metacognition
Learning
THRUSTS
Cognitive Factors
Metacognition &
Motivation
Social Communication
Comp Modeling &
Data Mining
Expert
Knowledge
Knowledge:
Deep,
conceptual,
fluent
Metacognition
Motivation
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Thrust Research Questions
• Cognitive Factors. How do instructional events affect
learning activities and thus the outcomes of learning?
• Metacognition & Motivation. How do activities
initiated by the learner affect engagement with targeted
content?
• Social Communication. How do interactions between
learners and teachers and computer tutors affect
learning?
• Computational Modeling & Data Mining. Which
models are valid across which content domains, student
populations, and learning settings?
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4th Measure of Robust
Learning
• Existing robust learning measures
– Transfer
– Long-term retention
– Acceleration of future learning
• New measure:
– Desire for future learning
• Is student engaged in subject?
• Do they chose to pursue further math, science, or
language?
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END
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