An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle School and High School Classrooms

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. Good Job! student agent Memphis Systems: K12 and College AutoTutor iSTART MetaTutor ARIES ALEKS - math IDRIVE Tutor Agent

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 Student input (answers, comments, questions) • Dialog history with • tutor turns • student turns AutoTutor

5. 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)

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 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

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 (Monologuewith 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 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)

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

18. 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

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 (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.

20. ?

21. Memphis City School Study IIDesign

22. 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

23. 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)

24. 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)

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. Comparison How 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 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?

28. Learning Environments with Agents developed at University of Memphis

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

30. Other Collaborations with Agents at University of Memphis

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

35. Force equals mass times acceleration

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 Interactive, Monologue, or Dialogue instruction Posttest

43. Memphis City School Study(342 students) 2 x 2 x 3 Design

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 IIVicarious Interface

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