Barry Gholson, Art Graesser, and Scotty Craig University of Memphis
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![Page 1: An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle School and High School Classrooms Barry Gholson, Art Graesser, and.](https://reader035.fdocuments.us/reader035/viewer/2022062423/56649e535503460f94b4969e/html5/thumbnails/1.jpg)
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
![Page 2: An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle School and High School Classrooms Barry Gholson, Art Graesser, and.](https://reader035.fdocuments.us/reader035/viewer/2022062423/56649e535503460f94b4969e/html5/thumbnails/2.jpg)
Memphis Systems: K12 and College
Good Job!
AutoTutor iSTART MetaTutor
ALEKS - math
Tutor Agent
student agent
ARIESIDRIVE
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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?
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Talking headGesturesSynthesized speech
Presentation of the question/problem
Dialog history with tutor turnsstudent turns
Student input (answers, comments, questions)
AutoTutor
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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)
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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.
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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.
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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).
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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)
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The Contest Interactive computer tutor (Interactive
condition) vs. Vicarious learning from dialogue with
deep reasoning questions (Dialogue condition)
vs. Monologue (Monologue condition)
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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
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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
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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)
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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
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Classroom Research
Standard classroom teaching
vs.
Vicarious learning from dialogue with deep reasoning questions
vs.
Monologue
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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)
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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
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Daily essay questions Memphis City School Study II
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Dialogue
Monologue
Co
hen
’s d
eff
ect
size
Effect size compared to standard classroom
Dialogue vs. standard
pedagogy
Monologue vs. standard
pedagogy
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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.
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?
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Memphis City School Study IIDesign
Class Format Conditions
1 vicariousMonologue
Dialogue
2 vicariousMonologue
Dialogue
3 interactiveRegular
classroom instruction
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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
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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)
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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)
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Alternative Predictions
1. Interactive hypothesis: Interactive > Q-Dialog = Monolog
2. Dialogic hypothesis: Interactive = Q-Dialog > Monolog
3. Deep question hypothesis:Q-Dialog > Interactive ≥ Monolog
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Learning Conceptual Physics
Four conditions:
• Read Nothing
• Read Textbook
• AutoTutor
• Human Tutor0.5 0.6 0.7 0.8
Adjusted post-test scores
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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?
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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
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Memphis City School Study IResults - Overall
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Interactive
Dialogue
Monologue
Coh
en’s
d
Cohen’s d effect size
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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
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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
![Page 32: An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle School and High School Classrooms Barry Gholson, Art Graesser, and.](https://reader035.fdocuments.us/reader035/viewer/2022062423/56649e535503460f94b4969e/html5/thumbnails/32.jpg)
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?
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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.
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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
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Force equals mass times acceleration
Pretest
Essay
Pretest
MC
Training Posttest Essay
Posttest
MCAll-or-none
Learning
X00X X0XXXX0XXX XXX0XXX0XX
XX0X0X
X0XX0X1XX1 X1X1 XX1XX1XXXX
X11XXXXXX1
XX1XXX
Variable Learning
X10X X0XXXX0XXX XXX1XXX0XX
XX0X0X
X0XX1X1XX0 X1X0 XX1XX0XXXX
X11XXXXXX0
XX1XXX
No Learning
X00X X0XXXX1XXX XXX0XXX0XX
XX1X0X
X0XX0X1XX0 X1X0 XX0XX0XXXX
X10XXXXXX0
XX1XXX
Refresher Learning
X00X X0XXXX1XXX XXX1XXX1XX
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X1XX1X1XX1 X1X1 XX1XX1XXXX
X11XXXXXX1
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Conceptual Physics(Graesser, Jackson, et al., 2003)
Three conditions:
• AutoTutor
• Read textbook
• Read nothing
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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
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Impact of Dialog versus Monolog on recall and questions in a transfer task
(Craig, Gholson, Ventura, & Graesser, 2000)
10
12
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24
# id
eas
reca
lled
Trainingcontent
Transfercontent
Monolog Dialog
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# q
ues
tio
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aske
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Shallow Deep
Monolog Dialog
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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
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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?)
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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:
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Procedure
Gates-McGinitie reading test& Pretest
Posttest
Interactive, Monologue, or Dialogue instruction
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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
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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
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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
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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
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Memphis City School Study IIVicarious Interface
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Memphis City School Study II
• Question: How will the vicarious conditions perform next to interaction with a human teacher?