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1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Page 1: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Studying and achieving robust learning with PSLC resources

Ken KoedingerHCI & Psychology

CMU Director of PSLC

Page 2: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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This is the 4th Annual PSLC Summer School

• 8th overall– ITS was focus in

2001 to 2004• Goals:

– Learning science & technology concepts

– Hands-on project you present on Fri

Page 3: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

Pittsburgh Science of Pittsburgh Science of Learning Center (PSLC)Learning Center (PSLC)170+ researchers from CA to Germany

Ken Koedinger - Carnegie Mellon Co-Director Kurt VanLehn - University of Pittsburgh Co-DirectorCharles Perfetti - Chief Scientist & Future Co-Director

Executive Committee: Vincent Aleven, Maxine Eskenazi (Diversity Director), Julie Fiez, David Klahr (Education Director), Marsha Lovett, Tim Nokes, Lauren Resnick+ representatives of Jr. Faculty, Post-docs & Grad students

Michael Bett – Managing Director

Page 4: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Advancing a Science of Academic LearningChallenge: Chasm between learning

science & educational practice • Empirical

– Lots of rigorous principle-testing lab studies– Lots of realistic classroom design research– Too few experiments combine both

• Theoretical– Almost as many theories as there are results!– Need for “rigorous, sustained scientific research in

education” (National Research Council, 2002)

Page 5: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Overview

• Background– Intelligent Tutoring Systems– Cognitive Task Analysis

• PSLC Methods, Resources, & Theory– In vivo experimentation– LearnLab courses– Robust learning theoretical framework– Enabling technologies

• Summary

Next

Page 6: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC is about much more than Intelligent Tutors

But tutors & course evaluations were a key inspiration

Quick review …

Page 7: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

• Algebra Cognitive Tutor – Part of complete course

Students: model problems with Students: model problems with diagrams, graphs, equationsdiagrams, graphs, equations

Tutor: feedback, help, Tutor: feedback, help, reflective dialogreflective dialog

Page 8: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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CognitiveTutorTechnology

Algebra I GeometryEquationSolver

Algebra II

Cognitive Tutors

ArtificialIntelligence

CognitivePsychology

Research base

Math InstructorsMath EducatorsNCTM Standards

Curriculum Content

Cognitive Tutor Approach

Page 9: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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• Cognitive Model: A system that can solve problems in the various ways students can

Strategy 1: IF the goal is to solve a(bx+c) = d THEN rewrite this as abx + ac = dStrategy 2: IF the goal is to solve a(bx+c) = d

THEN rewrite this as bx + c = d/a

Misconception: IF the goal is to solve a(bx+c) = d THEN rewrite this as abx + c = d

Cognitive Tutor Technology

Page 10: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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3(2x - 5) = 9

6x - 15 = 9 2x - 5 = 3 6x - 5 = 9

Cognitive Tutor Technology

• Cognitive Model: A system that can solve problems in the various ways students can

If goal is solve a(bx+c) = dThen rewrite as abx + ac = d

If goal is solve a(bx+c) = dThen rewrite as abx + c = d

If goal is solve a(bx+c) = dThen rewrite as bx+c = d/a

• Model Tracing: Follows student through their individual approach to a problem -> context-sensitive instruction

Page 11: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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3(2x - 5) = 9

6x - 15 = 9 2x - 5 = 3 6x - 5 = 9

Cognitive Tutor Technology

• Cognitive Model: A system that can solve problems in the various ways students can

If goal is solve a(bx+c) = dThen rewrite as abx + ac = d

If goal is solve a(bx+c) = dThen rewrite as abx + c = d

• Model Tracing: Follows student through their individual approach to a problem -> context-sensitive instruction

Hint message: “Distribute a across the parentheses.”

Bug message: “You need tomultiply c by a also.”

• Knowledge Tracing: Assesses student's knowledge growth -> individualized activity selection and pacing

Known? = 85% chance Known? = 45%

Page 12: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Cognitive Tutor CourseDevelopment Process1. Client & problem identification2. Identify the target task & “interface”3. Perform Cognitive Task Analysis (CTA)4. Create Cognitive Model & Tutor

a. Enhance interface based on CTAb. Create Cognitive Model based on CTAc. Build a curriculum based on CTA

5. Pilot & Parametric Studies6. Classroom Evaluation & Dissemination

Page 13: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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CognitiveTutorTechnology

Algebra I GeometryEquationSolver

Algebra II

Cognitive Tutors

ArtificialIntelligence

CognitivePsychology

Research base

Math InstructorsMath EducatorsNCTM Standards

Curriculum Content

Cognitive Tutor Approach

Page 14: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

Page 15: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Algebra Student Results:Story Problems are Easier!

70%61%

42%

0%

20%

40%

60%

80%

Story Word Equation

Problem Representation

Percent Correct

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.

Page 16: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Expert Blind Spot:Expertise can impair judgment of student difficulties

0

10

20

30

40

50

60

70

80

90

100

ElementaryTeachers

MiddleSchool

Teachers

High SchoolTeachers

% makingcorrectranking

(equations hardest)

Page 17: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

Page 18: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Cognitive Tutor CourseDevelopment Process1. Client & problem identification2. Identify the target task & “interface”3. Perform Cognitive Task Analysis (CTA)4. Create Cognitive Model & Tutor

a. Enhance interface based on CTAb. Create Cognitive Model based on CTAc. Build a curriculum based on CTA

5. Pilot & Parametric Studies6. Classroom Evaluation & Dissemination

Page 19: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Tutors make a significant difference in improving student learning!

• 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!!

0

10

20

30

40

50

60

Iowa SAT subset ProblemSolving

Represent-ations

Traditional Algebra Course

Cognitive Tutor Algebra

Page 20: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

Page 21: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Logic of Pittsburgh Science of Learning Center (PSLC)• Support experimental studies that

– Test fundamental principles, not whole courses

– Are internally & externally valid

• Create a theory of “robust learning”• Leverage technology &

computational modeling

Page 22: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Overview

• Background– Intelligent Tutoring Systems– Cognitive Task Analysis

• PSLC Methods, Resources, & Theory– In vivo experimentation– LearnLab courses– Robust learning theoretical framework– Enabling technologies

• Summary

Next

Page 23: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC statement of purpose

• To yield theoretically sound and useful principles of robust learning, we have created LearnLab to facilitate in vivo learning experimentation.

Outline of Overview

1. Robust learning

2. Theory

3. Method

4. LearnLab

Next

Page 24: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

Page 25: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC statement of purpose

• To yield theoretically sound and useful principles of robust learning, we have created LearnLab to facilitate in vivo learning experimentation.

Outline of Overview

1. Robust learning

2. Theory

3. Method

4. LearnLab

Next

Page 26: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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In Vivo Experiments Principle-testing laboratory quality in real classrooms

Page 27: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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In Vivo Experimentation Methodology

Methodology features:• What is tested?

– Instructional solution vs. causal principle

• Where & who?– Lab vs. classroom

• How?– Treatment only vs.

Treatment + control

Lab experiments

Design research

& field trials

Instructional solution

Cla

ssro

om

La

b

Causal principle

What is tested?

Wh

e re ?

• Generalizing conclusions:– Ecological validity: What instructional

activities work in real classrooms?– Internal validity: What causal

mechanisms explain & predict?

Page 28: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Methodology features:• What is tested?

– Instructional solution vs. causal principle

• Where & who?– Lab vs. classroom

• How?– Treatment only vs.

Treatment + control

Lab experiments

Design research

& field trials

In Vivo Experimentation Methodology

In Vivo learning

experiments

Instructional solution

Causal principle

What is tested?

Wh

e re ?

• Generalizing conclusions:– Ecological validity: What instructional

activities work in real classrooms?– Internal validity: What causal

mechanisms explain & predict?

Cla

ssro

om

La

b

Page 29: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC statement of purpose

• To yield theoretically sound and useful principles of robust learning, we have created LearnLab to facilitate in vivo learning experimentation.

Outline of Overview

1. Robust learning

2. Theory

3. Method

4. LearnLab

Next

Page 30: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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LearnLabA Facility for Principle-Testing

Experiments in Classrooms

Page 31: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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 labChemistry virtual lab

Physics intelligent tutorPhysics intelligent tutor

REAP vocabolary tutorREAP vocabolary tutor

Page 32: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC resources enable theoretical development …

Page 33: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC statement of purpose

• To yield theoretically sound and useful principles of robust learning, we have created LearnLab to facilitate in vivo learning experimentation.

Outline of Overview

1. Robust learning

2. Theory

3. Method

4. LearnLab

Next

Page 34: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Theoretical Framework Levels• Macro level

– What instructional principles explain how changes in the instructional environment lead to changes in robust learning?

• Micro level

Page 35: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Explanation at the macro-level

Novice knowledge state

Learning Processes

Instructional Method IT vs. IC

Pre-test Accelerated future learning

Transfer Long-term retention

Robust Learning measures:

Expert knowledge state

Normal Post-test

Why is instruction IT better than IC?

Page 36: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Hausmann & VanLehn 2007 study: Macro level description• Research question:

Does providing explanations or eliciting “self-explanations” from students better enhance robust learning?

• General instruction: Students alternate between– Watching videos of worked examples of physics

problems– Solving new problems in the Andes intelligent tutor

• Treatment variables: – Videos include justifications for steps or do not– Students are prompted to “self-explain” or paraphrase

Page 37: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Self-explanations => greater robust learning

0.670.670.90 0.980

0.2

0.4

0.6

0.8

1

1.2

Paraphrase,With just.

Paraphrase,Without just.

Self-explain,With just.

Self-explain,Without just.

(hints+errors) / steps

• Justifications: no effect!

• Immediate test on electricity problems:

• Transfer to new electricity homework problems

0.450.370.69 1.040

0.2

0.4

0.6

0.8

1

1.2

1.4

Paraphrase,With just.

Paraphrase,Without just.

Self-explain,With just.

Self-explain,Without just.

(hints+errors) / steps

• Instruction on electricity unit => accelerated future learning of magnetism!

0.911.17 0.75 0.680

0.2

0.4

0.6

0.8

1

1.2

1.4

Paraphrase,With just.

Paraphrase,Without just.

Self-explain,With just.

Self-explain,Without just.

(hints+errors) / steps

Page 38: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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)

Page 39: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Theory Integration Strategy

• PSLC has supported some 130+ studies

• How do they fit together?

Page 40: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Robust Learning

Sense-Making

Foundational Skills

Macro-level of robust learning framework: Taxonomy of outcomes, processes, & treatments

Outcomes:

Instructional Treatments:

Collaboration, tell vs. ask, question insertion

Visual-verbal coordination, example-rule coordination

Explicit instruction,

learner control…

Schedules, …

Construction, elaboration,

discriminationCo-Training Refinement

of FeaturesStreng-thening

Learning Processes:

Page 41: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Robust Learning

Sense-Making

Foundational Skills

3 Original Research clusters

Outcomes:

Instructional Treatments:

Collaboration, tell vs. ask, question insertion

Visual-verbal coordination, example-rule coordination

Explicit instruction,

learner control…

Schedules, …

Construction, elaboration,

discriminationCo-Training Refinement

of FeaturesStreng-thening

Learning Processes:

Interactive Interactive CommunicationCommunication

Coordinative Coordinative LearningLearning

Fluency and Fluency and RefinementRefinement

Page 42: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Robust Learning

Sense-Making

Foundational Skills

Hausmann & VanLehn in Interactive Communication Cluster

Outcomes:

Instructional Treatments:

Collaboration, tell vs. ask, question insertion

Explicit instruction,

learner control…

Schedules, …

Visual-verbal coordination, example-rule coordination

Construction, elaboration,

discriminationCo-Training Refinement

of FeaturesStreng-thening

Learning Processes:

Interactive Interactive CommunicationCommunication

Coordinative Coordinative LearningLearning

Fluency and Fluency and RefinementRefinement

Page 43: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Interactive Communication Research Question• What properties of interactive

communication promote robust learning?

– 20+ projects/studies have addressed versions of this question

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Use of wiki toward macro-level theory developmentExperimenters & instructors:• Create study summary pages on wiki• Work out shared meanings of terms

– Like, what does “self-explanation” mean?– Create & edit glossary pages in the wiki

• Seek out commonalities with others – Tie their hypotheses to general principles– Create shared instructional principle pages

Page 45: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

PSLC wiki supports theory integration

Links to H&V study page:Interactive Communication cluster page:

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Page 46: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC wiki supports theory integration

182 concepts in glossarySelf-explanation glossary entry

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Page 47: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC wiki supports theory integration

Points back to Hausmann’s study page

Instructional Principle pages unify across studies

Page 48: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Theoretical Framework Levels• Macro level

– What instructional principles explain how changes in the instructional environment lead to changes in robust learning?

• Micro level– Can learning be explained in terms of what

knowledge components are acquired at individual learning events?

Page 49: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Key Micro-level Concepts

• Knowledge component (unobservable)– A mental structure or process that a learner uses to

accomplish steps in a task or a problem

• Learning event (unobservable)– Points in time where knowledge components are

learned or used

• Instructional event (observable)– Element of learning environment designed to evoke

a particular learning event– P(Learning_event | Instructional_event) < 1.0

• Learner factors like metacognition & motivation affect this conditional probability

Page 50: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Kinds of Knowledge Components (KCs)Mental representations of:• Domain knowledge

– Facts, concepts, principles, rules, procedures, strategies

• Prerequisite knowledge– Feature encoding knowledge

• Integrative knowledge– Schemas or procedures that

connect other KCs• Metacognitive knowledge

– About knowledge or controlling use of knowledge

• Beliefs & interests– What one likes, believes

• Cross-cutting distinctions– Correct vs. incorrect – Verbal (explicit) vs. non-verbal

(implicit)– Probabilistic vs. discrete

NOT KCs:• Any external representation of

knowledge– Textbook descriptions

• Generic cognitive structures– Working memory

• Continuous parameters on knowledge representations

– Strength, level of engagement, implicit value of a goal, affect

Page 51: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Macro-level analysis treats knowledge states & learning processes as black boxes

Novice knowledge state

Learning Processes

Instructional Method IT vs. IC

Pre-test Accelerated future learning

Transfer Long-term retention

Robust Learning measures:

Expert knowledge state

Normal Post-test

Page 52: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Macro-level analysis treats knowledge states & learning processes as black boxes

Novice knowledge state

Learning Processes

Instructional Method IT vs. IC

Pre-test Accelerated future learning

Transfer Long-term retention

Robust Learning measures:

Expert knowledge state

Normal Post-test

Page 53: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Unpacking learning: 3 kinds of events

Novice knowledge state

Pre-testRobust Learning measures

Expert (desired) knowledge state

Normal Post-test

Learning Event

Instructional Event

Assessment event

Learning Event

Instructional Event

Assessment event

Learning Event

Instructional Event

Page 54: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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PSLC’s DataShop

• Vast open data repository, analysis tools, visualizations

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

• Generates:– learning curves– statistical model

fit (blue line)

• Based on micro level analysis:– learning event

opportunities– Averaged across

knowledge components

Page 55: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Greatest impact of technology on 21st century education?

• Benefits to student learning from use of educational technology?

• Perhaps• But my bet is:

– Scientific advances coming from data mining of the vast explosion of learning data that will be coming from educational technologies

Page 56: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Problem1 Problem2 Problem3

(hints+errors)/steps

Instructionalexplanation

Self-explanation

Example1 Example2 Example3

Back to H&V study: Micro-analysisLearning curve for main KCSelf-explanation effect tapers but not to zero

Page 57: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Overview

• Background– Intelligent Tutoring Systems– Cognitive Task Analysis

• PSLC Methods, Resources, & Theory– In vivo experimentation– LearnLab courses– Robust learning theoretical framework– Enabling technologies

• Summary Next

Page 58: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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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

Page 59: 1 Studying and achieving robust learning with PSLC resources Ken Koedinger HCI & Psychology CMU Director of PSLC

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Summary• Learning can be greatly improved!

– Address robust learning– Increase certainty– Leverage technology

• PSLC provides tools & processes to help learning researchersachieve these goals

RobustLearning

Principles

Existing SoloTheories

In VivoStudies

EnablingTechnology

LearnLabCourses

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END