1. 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

2. Memphis Systems: K12 and College Good Job! AutoTutoriSTARTMetaTutor ALEKS - math Tutor Agent student agent ARIES IDRIVE

3. 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?

4. Talking head  Gestures  Synthesized speech  Presentation of the question/problem Dialog history with  tutor turns  student turns Student input (answers, comments, questions) AutoTutor

5. 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)

6. 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.

7. 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.

8. 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).

9. 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)

10. The Contest Interactive computer tutor (Interactive condition) vs. Vicarious learning from dialogue with deep reasoning questions (Dialogue condition) vs. Monologue (Monologue condition)

11. 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

12. Laboratory results with multiple choice data Craig, Sullins, Witherspoon, & Gholson, (2006). Cognition & Instruction. College students and computer literacy Three Conditions: –Interactive (AutoTutor) –Yoked vicarious (AutoTutor sessions) –Q-Dialogue with deep questions Yoked Vicarious Dialogue Interactive Cohen’s d effect size

13. 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)

14. Impact of condition as a function of prior knowledge Memphis City School Study I Cohen’s d effect size

15. Classroom Research Standard classroom teaching vs. Vicarious learning from dialogue with deep reasoning questions vs. Monologue

16. Overview of biology study Memphis City School Study II 8 th 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)

17. Azevedo and Gholson test results Memphis City School Study II Cohen’s d effect size Mental model shift

18. Daily essay questions Memphis City School Study II Cohen’s d effect size Effect size compared to standard classroom Dialogue vs. standard pedagogy Monologue vs. standard pedagogy

19. 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 (8 th - 11 th ). 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.

20. ?

21. Memphis City School Study II Design ClassFormatConditions 1vicarious Monologue Dialogue 2vicarious Monologue Dialogue 3interactive Regular classroom instruction

22. Memphis City School Study II Using vicarious learning to teach course content at Snowden Middle School 8 th Graders Our first foray into the circulatory system domain

23. Memphis City School Study II Materials 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)

24. Memphis City School Study II Procedure 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)

25. Alternative Predictions 1. Interactive hypothesis: Interactive > Q-Dialog = Monolog 2. Dialogic hypothesis: Interactive = Q-Dialog > Monolog 3. Deep question hypothesis: Q-Dialog > Interactive ≥ Monolog

26. Learning Conceptual Physics Four conditions: Read Nothing Read Textbook AutoTutor Human Tutor

27. 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?

28. 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

29. Memphis City School Study I Results - Overall Cohen’s d Cohen’s d effect size

30. Other Collaborations with Agents at University of Memphis iDRIVE Question answering in science & technology Gholson MetaTutor Metacognition in scienceAzevedo iMAP Multichannel commun ication with maps Louwerse SEEK Critical stance while exploring web Wiley, Goldman ARIES Critical Reasoning in scienceMillis, Britt, Magliano, Wiemer-Hastings

31. 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

32. 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?

33. 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.

34. Misconceptions contact forces exerted after contact ceases vertical forces might have a non-zero horizontal component heavier objects fall faster heavier objects accelerate faster for the same non-gravitational force air resistance non negligable freefall means constant velocity lighter object exerts no force on a larger object nonzero net force but no acceleration same force means same acceleration regardless of mass action and reaction force acts on same body 0 force implies slowing down 0 force implies speeding up 0 force implies 0 velocity (no autotutor equiv) 0 acceleration implies 0 velocity action and reaction force do not have same magnitude After an object is dropped or thrown the only force acting on it is gravity Gravitational force acts *only* in the vertical direction Inanimate 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

35. Force equals mass times acceleration Pretest Essay Pretest MC TrainingPosttest Essay Posttest MC All-or-none Learning X00XX0XXXX0XXX XXX0XXX0XX XX0X0X X0XX0X1XX1X1X1X1X1XX1XX1XXXX X11XXXXXX1 XX1XXX Variable Learning X10XX0XXXX0XXX XXX1XXX0XX XX0X0X X0XX1X1XX0X1X0X1X0XX1XX0XXXX X11XXXXXX0 XX1XXX No Learning X00XX0XXXX1XXX XXX0XXX0XX XX1X0X X0XX0X1XX0X1X0X1X0XX0XX0XXXX X10XXXXXX0 XX1XXX Refresher Learning X00XX0XXXX1XXX XXX1XXX1XX XX1X1X X1XX1X1XX1X1X1X1X1XX1XX1XXXX X11XXXXXX1 XX1XXX

36. Conceptual Physics (Graesser, Jackson, et al., 2003) Three conditions: AutoTutor Read textbook Read nothing

37. 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

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

39. 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

40. 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?)

41. 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:

42. Procedure Gates-McGinitie reading test & Pretest Posttest Interactive, Monologue, or Dialogue instruction

43. Memphis City School Study (342 students) 2 x 2 x 3 Design Age Subject Condition DialogueMonologueInteractive 8 th & 9 th Computer Physics 10 th & 11 th Computer Physics

44. Multiple Choice Test Results Physics & Computer Literacy

45. 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

46. 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

47. Memphis City School Study II Vicarious Interface

48. Memphis City School Study II Question: How will the vicarious conditions perform next to interaction with a human teacher?