An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle

School and High School Classrooms

Barry Gholson, Art Graesser, and Scotty Craig

University of Memphis

Memphis Systems: K12 and College

Good Job!

AutoTutor iSTART MetaTutor

ALEKS - math

Tutor Agent

student agent

ARIESIDRIVE

Art Graesser (PI)

Zhiqiang Cai

Patrick Chipman

Scotty Craig

Don Franceschetti

Barry Gholson

Xiangen Hu

Tanner Jackson

Max Louwerse

Danielle McNamara

Andrew Olney

Natalie Person

Vasile Rus

• Learn by conversation in natural language

Graesser, A.C., Chipman, P., Haynes, B.C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions in Education, 48, 612-618.

VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P., Olney, A., & Rose, C.P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3-62.

What is AutoTutor?

Talking headGesturesSynthesized speech

Presentation of the question/problem

Dialog history with tutor turnsstudent turns

Student input (answers, comments, questions)

AutoTutor

LEARNING GAINS OF TUTORS

(effect sizes) .42 Unskilled human tutors

(Cohen, Kulik, & Kulik, 1982)

.80 AutoTutor (14 experiments)(Graesser and colleagues)

1.00 Intelligent tutoring systems

PACT (Anderson, Corbett, Aleven, Koedinger)Andes, Atlas (VanLehn)Diagnoser (Hunt, Minstrell)Sherlock (Lesgold)

(?) Skilled human tutors (Bloom, 1987)

Is an intelligent interactive tutor really needed?

• Vicarious Learning. Perhaps observing a scripted dialogue can be just as effective.

• Deep Questions. Perhaps a dialogue organized around deep questions may be just as effective.

Why Vicarious Learning?

• Observation is an important learning method– Recall (Baker-Ward, Hess, & Flannagan, 1990) – Language (Akhtar et al., 2001, Huston & Wright, 1998) – Cultural norms (Ward, 1971; Metge, 1984)

• Vicarious learning can be as effective as interactive learning.– Human tutoring if observers collaborate (Chi, Hausman, & Roy, in press; Craig,

Vanlehn, & Chi, 2007)

– Intelligent tutoring when guided by deep questions (Craig et al, 2006)

• Provides a cost effective method that can easily be integrated into classrooms.

Facts about Deep Questions

• Students and teachers are not inclined to ask deep questions (Dillon, 1988; Graesser & Person, 1994).

• Training students to ask deep questions facilitates comprehension (Rosenshine, Meister & Chapman, 1996).

• Vicarious learning is effective when students observe animated conversational agents asking deep questions (Craig, Gholson, Ventura, & Graesser, 2000; Craig, et al., 2006; Gholson & Craig, 2006).

Deep-level reasoning questions

• Deep-level reasoning question– A question that facilitates logical, causal, or goal-

oriented reasoning

• Example: Shallow vs. Deep questions– What is a type of circulation? (shallow)– What is required for Systemic Circulation to occur?

(deep)

The Contest Interactive computer tutor (Interactive

condition) vs. Vicarious learning from dialogue with

deep reasoning questions (Dialogue condition)

vs. Monologue (Monologue condition)

Q-Dialogue versus Monologue

Agent 1: The sun experiences a force of gravity due to the earth, which is equal in magnitude and opposite in direction to the force of gravity on the earth due to the sun.

Agent 2: How does the earth's gravity affect the sun?

Agent 2: How does the gravitational force of the earth affect the sun?

Agent 1: The force of the earth on the sun will be equal and opposite to the force of the sun on the earth

Laboratory results with multiple choice dataCraig, Sullins, Witherspoon, & Gholson, (2006). Cognition & Instruction.

• College students and computer literacy

• Three Conditions:– Interactive (AutoTutor)– Yoked vicarious

(AutoTutor sessions)– Q-Dialogue with deep

questions

0

0.5

1

1.5

2

2.5

Yoked Vicarious

Dialogue

Interactive

Co

hen

’s d

eff

ect

size

Memphis City School Study I

• Middle and high school students in two domains– Computer literacy: Grades 8 & 10– Physics: Grades 9 & 11

• Three Conditions:– Interactive (AutoTutor)

– Dialogue (Monologue with deep questions)

– Monologue (AutoTutor Ideal Answers)

Impact of condition as a function of prior knowledge

Memphis City School Study I

1.09

0.47

0.00

0.64

0.04

1.20

0.52

0.00

1.83

0 0.5 1 1.5 2

Lowknowledge

MediumKnowledge

HighKnowledge

Interactive

Dialogue

monologue

Cohen’s d effect size

Classroom Research

Standard classroom teaching

vs.

Vicarious learning from dialogue with deep reasoning questions

vs.

Monologue

Overview of biology studyMemphis City School Study II

• 8th grade biology (circulatory system) • Day 1

– Pretesting• Gholson (multiple choice) • Azevedo (matching, labeling, flow

diagram, mental model shift)

• Days 2-6 – 30-35 minutes of vicarious dialogue,

vicarious monologue, or standard classroom instruction

– 10 minutes to answer essay questions• Day 7

– 15-20 minutes of vicarious or interactive review

• Day 8 – Posttests

• Gholson (multiple choice) • Azevedo (matching, labeling, flow

diagram, mental model shift)

Azevedo and Gholson test resultsMemphis City School Study II

0 0.5 1 1.5 2

MultipleChoice

Matching

Labeling*

Flow Diagram

Standard classroom

Dialogue

Monologue

Cohen’s d effect size

0 0.5 1 1.5

Mentalmodelpretest

Mentalmodel

posttest

Mental model shift

Daily essay questions Memphis City School Study II

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Dialogue

Monologue

Co

hen

’s d

eff

ect

size

Effect size compared to standard classroom

Dialogue vs. standard

pedagogy

Monologue vs. standard

pedagogy

Conclusions

• Vicarious learning is effective when students observe animated conversational agents asking deep questions.

• Deep-level reasoning questions effect replicates in computer literacy and Newtonian Physics (8th-11th).

• Vicarious learning is most effective for learners with low domain knowledge.

• Vicarious learning transfers to classroom settings for daily essays, but not for the primarily more shallow one day delayed tests.

?

Memphis City School Study IIDesign

Class Format Conditions

1 vicariousMonologue

Dialogue

2 vicariousMonologue

Dialogue

3 interactiveRegular

classroom instruction

Memphis City School Study II

• Using vicarious learning to teach course content at Snowden Middle School

• 8th Graders

• Our first foray into the circulatory system domain

Memphis City School Study IIMaterials

• Students in vicarious conditions observe the virtual tutoring session via laptop computer in the classroom

• Students in the interactive condition receive the regular classroom instruction

• 2 Pretests developed by – Gholson (multiple choice)– Azevedo (matching, labeling, flow diagram, mental

model shift)• 3 Posttests developed by

– Gholson & Azevedo (identical to pretest)

Memphis City School Study IIProcedure

• Day 1– Pretesting

• Days 2-6 – 30-35 minutes of vicarious or interactive instruction in

the circulatory system– 10 minutes to answer review questions after

instruction• Day 7

– 15-20 minutes of vicarious or interactive review• Day 8

– Posttests (Gholson and Azevedo)

Alternative Predictions

1. Interactive hypothesis: Interactive > Q-Dialog = Monolog

2. Dialogic hypothesis: Interactive = Q-Dialog > Monolog

3. Deep question hypothesis:Q-Dialog > Interactive ≥ Monolog

Learning Conceptual Physics

Four conditions:

• Read Nothing

• Read Textbook

• AutoTutor

• Human Tutor0.5 0.6 0.7 0.8

Adjusted post-test scores

What are Deep-Level Reasoning Questions? (Graesser and Person,1994)

LEVEL 1: SIMPLE or SHALLOW 1. Verification Is X true or false? Did an event occur?2. Disjunctive Is X, Y, or Z the case?3. Concept completion Who? What? When? Where?4. Example What is an example or instance of a category?).

LEVEL 2: INTERMEDIATE 5. Feature specification What qualitative properties does entity X have?6. Quantification What is the value of a quantitative variable? How much? 6. Definition questions What does X mean?8. Comparison How is X similar to Y? How is X different from Y?

LEVEL 3: COMPLEX or DEEP9. Interpretation What concept/claim can be inferred from a pattern of data?10. Causal antecedent Why did an event occur? 11. Causal consequence What are the consequences of an event or state? 12. Goal orientation What are the motives or goals behind an agent’s action?13. Instrumental/procedural What plan or instrument allows an agent to accomplish a goal? 14. Enablement What object or resource allows an agent to accomplish a goal?15. Expectation Why did some expected event not occur?16. Judgmental What value does the answerer place on an idea or advice?

Learning Environments with Agents developed at University of Memphis

AutoTutor Understanding science & technology

MetaTutor Learning how to learn and think

iSTART Deep reading

SEEK True versus false information on the web

iDRIVE Deep question asking and answering

HURAA Reasoning about research ethics

ARIES Scientific reasoning

iMAP Multi-channel communication

Memphis City School Study IResults - Overall

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Interactive

Dialogue

Monologue

Coh

en’s

d

Cohen’s d effect size

Other Collaborations with Agents at University of Memphis

iDRIVE Question answering in science & technology

Gholson

MetaTutor Metacognition in science Azevedo

iMAP Multichannel commun ication with maps

Louwerse

SEEK Critical stance while exploring web

Wiley, Goldman

ARIES Critical Reasoning in science Millis, Britt, Magliano, Wiemer-Hastings

Conclusions and summary

• Deep-level question effect - Deep-level question dialog improves learning over an interactive session, yoked vicarious session, & monolog session with same content

–(Craig, et al., 2006)• Effect replicates in computer literacy and

Newtonian Physics.• Effect transfers to classroom settings

Questions in Newtonian physics

The sun exerts a gravitational force on the earth as the earth moves in its orbit around the sun. Does the earth pull equally on the sun? Explain why?

Expectations and misconceptions in Sun & Earth problem

EXPECTATIONS• The sun exerts a gravitational force on the

earth. • The earth exerts a gravitational force on the

sun. • The two forces are a third-law pair.• The magnitudes of the two forces are the same.MISCONCEPTIONS• Only the larger object exerts a force. • The force of earth on sun is less than that of

the sun on earth.

Misconceptionscontact forces exerted after contact ceasesvertical forces might have a non-zero horizontal componentheavier objects fall fasterheavier objects accelerate faster for the same non-gravitational forceair resistance non negligablefreefall means constant velocity lighter object exerts no force on a larger objectnonzero net force but no acceleration same force means same acceleration regardless of massaction and reaction force acts on same body0 force implies slowing down0 force implies speeding up 0 force implies 0 velocity(no autotutor equiv) 0 acceleration implies 0 velocityaction and reaction force do not have same magnitudeAfter an object is dropped or thrown the only force acting on it is gravityGravitational force acts *only* in the vertical directionInanimate object exerts no/less force in interaction Object that has been hit exerts no/less force in interaction Accelerations of both objects equal during interaction Only masses of part of compound body considered The force acting on a body is dependent on the mass of the body Action and reaction force have same directions Acceleration considered relative to accelerated reference frame

Force equals mass times acceleration

Pretest

Essay

Pretest

MC

Training Posttest Essay

Posttest

MCAll-or-none

Learning

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XX1XXX

Variable Learning

X10X X0XXXX0XXX XXX1XXX0XX

XX0X0X

X0XX1X1XX0 X1X0 XX1XX0XXXX

X11XXXXXX0

XX1XXX

No Learning

X00X X0XXXX1XXX XXX0XXX0XX

XX1X0X

X0XX0X1XX0 X1X0 XX0XX0XXXX

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Refresher Learning

X00X X0XXXX1XXX XXX1XXX1XX

XX1X1X

X1XX1X1XX1 X1X1 XX1XX1XXXX

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Conceptual Physics(Graesser, Jackson, et al., 2003)

Three conditions:

• AutoTutor

• Read textbook

• Read nothing

Impact of Monolog versus Dialog on recall and questions in a transfer task

(Craig, Gholson, Ventura, & Graesser, 2000)

• Learning about computer literacy with conversational agents.– Monolog on computer literacy content– Dialog with added deep questions

• Recall of content in training task• Transfer tasks on new material

– Students instructed to generate questions about new computer literacy topics

– Recall of content of new material

Impact of Dialog versus Monolog on recall and questions in a transfer task

(Craig, Gholson, Ventura, & Graesser, 2000)

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# id

eas

reca

lled

Trainingcontent

Transfercontent

Monolog Dialog

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Shallow Deep

Monolog Dialog

Managing One AutoTutor Turn

• Short feedback on the student’s previous turn

• Advance the dialog by one or more dialog moves that are connected by discourse markers

• End turn with a signal that transfers the floor to the student– Question

– Prompting hand gesture

– Head/gaze signal

Expectation and Misconception-Tailored Dialog:

Pervasive in AutoTutor & human tutors • Tutor asks question that requires explanatory reasoning

• Student answers with fragments of information, distributed over multiple turns

• Tutor analyzes the fragments of the explanation– Compares to a list of expected good idea units– Compares to a list of expected errors and misconceptions

• Tutor posts goals & performs dialog acts to improve explanation– Fills in missing expected good idea units (one at a time)– Corrects expected errors & misconceptions (immediately)

• Tutor handles periodic sub-dialogues– Student questions– Student meta-communicative acts (e.g.,

What did you say?)

Dialog Moves During Steps 2-4

Positive immediate feedback: “Yeah” “Right!” Neutral immediate feedback: “Okay” “Uh huh” Negative immediate feedback: “No” “Not quite”

Pump for more information: “What else?” Hint: “What about the earth’s gravity?” Prompt for specific information: “The earth exerts a gravitational

force on what?” Assert: “The earth exerts a gravitational force on the sun.”

Correct: “The smaller object also exerts a force. ” Repeat: “So, once again, …” Summarize: “So to recap,…” Answer student question:

Procedure

Gates-McGinitie reading test& Pretest

Posttest

Interactive, Monologue, or Dialogue instruction

Memphis City School Study(342 students)

2 x 2 x 3 Design

Age Subject ConditionDialogue Monologue Interactive

8th & 9th

Computer

Physics

10th & 11th

Computer

Physics

0.2

0.25

0.3

0.35

0.4

Pretest Posttest Adjusted Posttest

Mu

ltip

le C

ho

ice

Te

st

Sc

ore

Monologue Dialogue Interactive

Multiple Choice Test ResultsPhysics & Computer Literacy

How to cover a single expectation

The earth exerts a gravitational force on the sun.

• Who articulates it: student, tutor, or both?• Fuzzy production rules drive dialog moves• Progressive specificity drives dialog moves

Hint Prompt Assertion cycles

• Strategies tailored to student knowledge and abilities

How does AutoTutor compare to comparison conditions on tests of deep comprehension?

• 0.80 sigma compared to pretest, doing nothing, and reading the textbook

• 0.22 compared to reading relevant textbook segments

• 0.07 compared to reading succinct script• 0.13 compared to AutoTutor delivering speech

acts in print• 0.08 compared to humans in computer-mediated

conversation• -0.20 compared to AutoTutor enhanced with

interactive 3D simulation • ZONE OF PROXIMAL DEVELOPMENT

Memphis City School Study IIVicarious Interface

Memphis City School Study II

• Question: How will the vicarious conditions perform next to interaction with a human teacher?