Daniel Schwartz Stanford University

1. Interactivity and Learning Daniel Schwartz Stanford University

2. I don’t mean to be vain, but does this really look like me?

3. Interactivity and Learning • Some types of learning do not seem to be the sort of thing that the representations and interactivity of the computer will be particularly helpful for.

4. Artificial Intelligence in Education • Two main capacities of interest: • Social Interactivity • AI techniques create socially inspired interactions • Learning in Formal Domains • Math, science, and other things that can be well modeled. • Soon, there will be sufficient models of social interaction that we can help students learn valued social behaviors. (Many attempts are underway.)

5. Goal of the talk • Present some history of research on valued social interactions – which is largely about motivation and attitude change. • Describe two dimensions of valued social interactions – which are largely about learning. • Present some suggestive research that my colleagues have done with teachable agents. • Discuss some academic history as to what counts as valuable learning. • Describe how modeling social interactions can yield new possibilities for valuable learning.

6. Interactivity and Learning • Goals of social interaction research • Enhancing social interaction for learning • Teachable Agents • Relevant Evidence using the Agents • Goals of learning research • Sweet Spot of Social Interaction and Learning

7. Early research on social interaction • For some, the goal of studying or controlling interaction is to improve human interaction per se. • This includes the research on cooperation that spawned cooperative learning. Interactivity Valued Interactivity

8. How to Create Productive Interactions? • Research in response to WWII • The goal was conflict resolution and cooperation • Morton Deutsch, 1973 “I started my graduate career not long after Hiroshima and Nagasaki, and my work in social psychology has been shadowed by the atomic cloud ever since. The efforts reported in this book reflect my continuing interest in contributing to the understanding of how to prevent destructive conflict and initiate cooperation.”

9. Led to Educational Applications • About motivation and attitude. • Content learning not the target so much as “attitude change” and “motivation management.” • Still, led to applications for learning math, science, reading. • Begins from assumption of potential conflict or withdrawal. • Not such a bad assumption for many school settings (in U.S.).

10. Applications to learning. • If students work cooperatively, they might improve their learning. • Two key conditions from cooperation research: • Mutual Interdependence • Individual Accountability • Slavin’s (1996) meta-analysis on cooperative learning: • MI or IA = +.07 effect size • MI & IA = +.32 effect size • Unfortunately, only 25% of teachers who are trained implement both. (Antil et al., 1998).

11. Shift to collaboration • More recently, interest in collaboration. • Collaboration is cooperation in the absence of serious conflict. • Collaboration is a valued form of interaction. • But, it does not necessarily mean people will learn well. • Barron et al. (1998) study…

12. In summary • The goal of much research is to promote valued interactions per se. • Exploration of motivations for positive interactions. • When borrowed by education, leads to a model: • Motivation  Valued Interactions ( Learning) • The motivations are for interactions, and not learning • Need to understand valued interactions that directly “motivate” learning.

13. OUTLINE HERE

14. Valued Interactionsfor Learning • The umbrella of valued interactions (Deutsch) • “A cooperative process is characterized by open and honest communication of relevant information among participants. Each is interested in informing, and being informed by, the other.” • What conditions turn this into learning?

15. Two dimensions • Incorporation of Ideas • The degree to which participants’ ideas are taken up by one another. • Initiative in Action • The degree to which all participants’ can initiate actions.

16. High motivation when achieved. • Take the example of conversation: • People like to talk. • People like to make the effort after shared meaning. • The effort to produce and share meaning. • We want others to incorporate the ideas we initiate. • We want to incorporate the ideas that others initiate (even if just to disagree or elaborate).

17. Positive Instances • Think of an animated conversation… • The world drops away. • Try to persuade others to understand and incorporate your ideas. • Listen to how they uptake your ideas and reflect them back. • Listen to their ideas and reflect them back, combined with your own ideas. • A talk is a slow version (stylized turn taking). • Writing a journal article is a really slow version. • Gossip is a favorite version.

18. Negative Instances • No chance to take the initiative • blocked from entering conversation • told what to do or say • listening with no prospect of action • No chance to incorporate your ideas • talking to people who do not understand • talking to people who cannot respond • ignored during conversation

19. Other Copying Watching Incorporation (ideas) Merged Being Imitated Showing Self Other Self Mixed Initiative (actions) Interactivity Space for Novice Learningin Motivating Collaborations Optimal Learning For Novices

21. Not just low motivation…also, low learning. • No mutual incorporation of ideas: • Neither teacher nor student learns. • No sharing of the initiative: • Neither teacher nor student learns.

22. Other Incorporation (ideas) Merged Self Other Self Mixed Initiative (actions) Examples of optimal region Optimal Learning For Novices

23. Computer Technologies • Present some research relevant to the two dimensions of interactivity. • I’ll look at teaching as the example • As last movie clip showed, not all teaching yields ideal social interaction and learning. • We use computers to help optimize the balance of incorporation and initiative.

24. Teachable Agents • Learning By Teaching • Common wisdom • people “really” learn when they teach. • Empirical findings • Students who prepare to teach learn more than students who prepare to take a test. (Bargh & Schul,1980; Biswas, et al., 2001) • Built computer agents that students teach • A natural social interaction students know well • Teach – Test – Remediate

25. Basic Teaching Interaction • Not machine induction; students must explicitly teach. • Students teach agent. • Students uses visual representations to teach the agent. • Agent performs based on teaching. • Generic AI algorithms draw inferences based on student teaching. • Students revise agent to do better. • Based on agent performance student revises agent and own knowledge.

26. Betty: A Teachable Agent

27. Extensions to TA paradigm • Students know they are not real people. • We are more interested in enabling social interactions that facilitate learning. • The well-known teaching schema works well. • Plus, once the basic interaction is developed, they can be extended in numerous ways.

28. Videogames(Kristen Blair) • Students teach agent to perform in game. • Besides motivation and game leveling, it enables a number of learning resources

29. On-Line Homework Game Show(Paula Wellings) • Students can log on, chat, and do homework with whomever is on-line. • Teach agent, who performs in a gameshow.

33. Front of the Class System(Joan Davis) • Students create models that can answer all questions, instead of memorizing a few answers to select problems. • Present results at front of class.

34. A Suite of Homely Teachable Agent “Engines” • Betty • Qualitative Reasoning • Orbo • Reasoning by Assumption • Milo • Reasoning by Model • Moby • Hypothetico-Deductive Reasoning • J-Mole • Reasoning by Discrepancy

35. Also, a suite of homely collaborators • In order of homeliness: • Gautam Biswas • John Bransford • Krittaya Leelawong, Thomas Katzleberger, Ying Bin • Joan Davis, Kristen Blair, Paula Wellings, George Chang • Girija Mittagunta, Elliot Castillo, Anh Huynh, Nancy Vye

36. Return to proposal about valued interactions that promote learning. Other Optimal Learning For Novices Incorporation (ideas) Merged Self Other Self Mixed Initiative (actions)

37. Incorporation • Agents, by design, merge ideas with students. • Students provide facts of the matter. • Agent provides spatial representation and reasoning. • Not just learning the brute facts, learning how the “expert” thinks with those facts. • Literally making thinking visible. • Hope is that merging with Betty’s representations and reasoning will lead student to learn and adopt those representations.

38. Interactivity Space for Novice Learning Agent Would students learn causal structure when ideas get merged with agent? Incorporation (ideas) Merged Student Agent Student Mixed Initiative (actions)

39. Merging Ideas • Undergraduates read exercise physiology text. • 8 Taught Betty on cell metabolism. • 8 Wrote Summary on cell metabolism. • Would students adopt Betty’s knowledge structure?

40. Direction of Causality During activity: Betty students discovered they had confused causation and correlation. Mitochondria <-> ATP synthesis Summary students tended to focus on outlining

41. Multiple Causality Given a metabolism word, list entities related to it. Simple Link: Mitochondria increase ATP synthesis. Complex Link:Mitochondria with glycogen or free fatty acids increase ATP synthesis.

42. Dynamic aspect of thinking • Merging is not just of representations, but also of reasoning. • Wanted to examine if the AI-reasoning component was important for merging ideas ideas. • 4th-grade students learned about pond ecology over three days. • Animation condition: • Taught Betty and she could answer their questions. • No Animation condition: • Created concept map using Betty (reasoning turned off)

43. Adoption of Causal Structure

44. Opportunities to commingle thoughts with agent helped students learn/adopt causal structure. • How about initiative?

45. Interactivity Space for Novice Learning Does mixing initiative with agent improve student learning? Agent Incorporation (ideas) Merged Student Agent Student Mixed Initiative (actions)

46. Independent Performance • Conversation is often taken as model of social interaction • Mixed-initiative involves shared lead. • A broader view extends interaction over time. • Mixed-initiative can include independent performance. • Teaching and then watching one’s student perform. • Student incorporates the teacher’s ideas, but also has to have abilities to do an independent performance. • Swedish dissertation defenses? • A very powerful form of mixed initiative. • Motivating • Excellent opportunity for teacher to learn. • Examine value of mixed-initiative as independent performance with a second agent.

47. Moby(Anh Huynh) To teach science content using hypothetico-deductive reasoning.

49. Green and Not Pink are Necessary for a Flower (Shade and ~ Sun are Necessary for a Flower)