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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. Good Job!. student agent. Memphis Systems: K12 and College. AutoTutor. iSTART. MetaTutor. ARIES.

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Barry gholson art graesser and scotty craig university of memphis

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!

student agent

Memphis Systems: K12 and College

AutoTutor

iSTART

MetaTutor

ARIES

ALEKS - math

IDRIVE

Tutor Agent


What is autotutor

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?


Barry gholson art graesser and scotty craig university of memphis

  • Talking head

  • Gestures

  • Synthesized speech

  • Presentation of the question/problem

Student input (answers, comments, questions)

  • Dialog history with

  • tutor turns

  • student turns

AutoTutor


Learning gains of tutors effect sizes

LEARNING GAINS OF TUTORS(effect sizes)

.42Unskilled human tutors

(Cohen, Kulik, & Kulik, 1982)

.80AutoTutor (14 experiments)

(Graesser and colleagues)

1.00Intelligent 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

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

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

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 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

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

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


Barry gholson art graesser and scotty craig university of memphis

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

Dialogue

Interactive

Yoked

Vicarious

Cohen’s d effect size


Memphis city school study i

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 (Monologuewith deep questions)

Monologue(AutoTutor Ideal Answers)


Impact of condition as a function of prior knowledge memphis city school study i

Impact of condition as a function of prior knowledge Memphis City School Study I

Cohen’s d effect size


Classroom research

Classroom Research

Standard classroom teaching

vs.

Vicarious learning from dialogue with deep reasoning questions

vs.

Monologue


Overview of biology study memphis city school study ii

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 results memphis city school study ii

Azevedo and Gholson test resultsMemphis City School Study II

Mental model shift

Cohen’s d effect size


Daily essay questions memphis city school study ii

Daily essay questions Memphis City School Study II

Effect size compared to standard classroom

Cohen’s d effect size

Dialogue

vs. standard

pedagogy

Monologue

vs. standard

pedagogy


Conclusions

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.


Barry gholson art graesser and scotty craig university of memphis

?


Memphis city school study ii design

Memphis City School Study IIDesign


Memphis city school study ii

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 ii materials

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 ii procedure

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

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

Learning Conceptual Physics

Four conditions:

  • Read Nothing

  • Read Textbook

  • AutoTutor

  • Human Tutor


Barry gholson art graesser and scotty craig university of memphis

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. ComparisonHow is X similar to Y? How is X different from Y?

LEVEL 3: COMPLEX or DEEP

9. Interpretation What concept/claim can be inferred from a pattern of data?

10. Causal antecedentWhy did an event occur?

11. Causal consequenceWhat 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. ExpectationWhy 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

Learning Environments with Agents developed at University of Memphis


Memphis city school study i results overall

Memphis City School Study IResults - Overall

Cohen’s d

Cohen’s d effect size


Other collaborations with agents at university of memphis

Other Collaborations with Agents at University of Memphis


Conclusions and summary

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

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 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.


Misconceptions

Misconceptions


Force equals mass times acceleration

Force equals mass times acceleration


Conceptual physics graesser jackson et al 2003

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

Three conditions:

  • AutoTutor

  • Read textbook

  • Read nothing


Barry gholson art graesser and scotty craig university of memphis

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


Barry gholson art graesser and scotty craig university of memphis

Impact of Dialog versus Monolog on recall and questions in a transfer task (Craig, Gholson, Ventura, & Graesser, 2000)


Managing one autotutor turn

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

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

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

Procedure

Gates-McGinitie reading test

& Pretest

Interactive, Monologue,

or Dialogue instruction

Posttest


Memphis city school study 342 students

Memphis City School Study(342 students)

2 x 2 x 3 Design


Barry gholson art graesser and scotty craig university of memphis

Multiple Choice Test Results

Physics & Computer Literacy


How to cover a single expectation

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

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 ii vicarious interface

Memphis City School Study IIVicarious Interface


Memphis city school study ii1

Memphis City School Study II

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


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