Computer Supported Collaborative Learning Track Introduction

1. Computer Supported Collaborative Learning Track Introduction Carolyn Penstein Rosé Carnegie Mellon University Language Technologies Institute and Human-Computer Interaction Institute School of Computer Science

3. Track Helpers David Adamson dadamson@gmail.com Ryan Carlson rcarlson@cs.cmu.edu

4. Outline • Individual learning with technology to collaborative learning • Vision for dynamic collaborative learning support • Research Issues • Current Directions

5. Historical Perspective… • Socratic tutoring: directed lines of reasoning • Evidence that socratic tutoring is more beneficial than didactic tutoring (Rosé et al., 2001a) • Socratic style implemented in Knowledge Construction Dialogues (KCDs) • General attempt to model effective human tutoring • Hierarchical structure: adaptive to student needs • Used to elicit reflection • First used to support individual learning in Physics • Atlas-Andes (Rosé et al., 2001) • WHY-Atlas (Rosé et al., 2003; Rosé & VanLehn, 2005)

6. Empirical Foundation for CycleTalk • Human tutoring not always better than non-interactive support (VanLehn et al., 2007) • Focus shift to capturing what it is about interaction that is effective for instruction • Human tutors guide students towards opportunities for reflection (Rosé & Torrey, 2004) • Human tutor support by effective tutors is significantly better than hint based support (Rosé et al., 2005)

7. Towards enriching the interaction… • Student interaction with dialogue agents lacks elaboration (Rosé et al., 2003) • Students expect to behave differently with agents (Rosé & Torrey, 2004) • Students can benefit from working with another student, even in the absence of computer scaffolding (Gweon et al., 2006; Kumar et al., 2007)

8. VMT-Basilica ** Students learn up to 1.25 standard deviations more when interactive support is provided in the environment.

9. Labeled Texts Labeled Texts LightSIDE Behavior Unlabeled Texts A Model that can Label More Texts Time Monitoring Collaboration with Machine Learning Technology Download tools at: http://www.cs.cmu.edu/~emayfiel/side.html <Triggered Intervention>

10. Basilica Tutor Agent Design ConcertChat Server ConcertChatActor ConcertChatListener MessageFilter PresenceFilter DiscourseMemory AnnotationFilter OutputCoordinator SocialController ActivityDetector ProgressDetector PlanExecutor RequestDetector T.TakingCoordinator IntroductionsManager PromptingManager TutoringManager TutoringActor IntroductionsActor PromptingActor Kumar, R. & Rosé, C. P. (2011). Architecture for building Conversational Agents that support Collaborative Learning, IEEE Transactions on Learning Technologies special issue on Intelligent and Innovative Support Systems for Computer Supported Collaborative Learning

11. Computer Supported Learning in Large Classroom Studies • University Level • Freshman Engineering • Calculus • Sophomore Thermodynamics • High School • 9th Grade Biology • Earth Sciences • Middle School • Engineering Outreach • 6th grade math

12. First-Year and Middle School Projects First-Year Project Wrench Design Competition Part of Computer Aided Engineering Labs using Pro/ENGINEER, Pro/MECHANICA and Pro/MANUFACTURING Software (3 sessions) Students Work in Teams of 3 – 4 with Unknown Team Members in Other Parts of the Room Only Communication is via the Chat Interface with Agent Interactions Teach Relationships between Moment, Force and Stress Related to Design Goals • Middle-School • Practical Considerations in Designing a Wrench • A Simplified Version of the Freshman Project (Using the Pro/ENGINEER CAD Package) • Part of the CMU C-MITES Program • Students Work in Teams of 3 – 4 with Unknown Team Members in Other Parts of the Room • Activity Designed to Elicit Broad Discussions on Engineering and Product Design Middle School Students in the Wrench Design Project

13. Second-Year Thermodynamics Project • Sophomore Thermodynamics Course • Rankine Cycle Design Competition • Part of a Thermodynamic Cycles Lab (1 session) • Students Work in Teams of 2 with Their Partner in Another Part of the Room • Instructed on Effects of Changing System Variables (Temperature, Pressure) on System Output (Power, Waste Heat) • Goal: Increase Efficiency within Practical Constraints • Software Package is CyclePad (Developed at Northwestern University (Forbes et. al. 1999) to Allow Students to Construct and Analyze a Variety of Thermodynamic Cycles)

14. Thermo Collaborative Task • Goal: Design a power plant based on the Rankine Cycle paradigm • Each pair turns in exactly one design • Competing Student Goals: • Power: Design a power plant that achieves maximum power output • Motivated by economic concerns • Green: Design a power plant that has the minimum impact on the environment • Motivated by environmental concerns

15. Increasing Conceptual Depth • Student: so i think the best thing to go with first is to increase the pressure entering the connector • Tutor: You might want to review what you saw earlier in the sensitivity analysis investigating the effect of changing P-max on steam quality and efficiency, from the top half of page 13 in your book. • Tutor: type: HELP WITH CHANGEPMAX if you want to discuss it with me. • Student: help with changepmax • Tutor: Remember the sensitivity analysis you have completed where you manipulated P-Max and observed it's effect on the efficiency of the cycle. You can refer back to Figure 11 on page 13 of your booklet, which …

16. Example Interaction

17. Core Research Focus • Identify conversational interactions that are valuable for learning • Automatic conversation analysis • Facilitates learning research • Automates assessment of group processes • Enables context sensitive triggering of support • Interactive support technologies

18. Operationalization of Productive Talk for Learning • Drawn from multiple frameworks valuing sociocognitive engagement • Students explicitly display their reasoning • Students orient their contributions towards previous contributions • Students increase in their ownership of their ideas • Made precise through constructs from linguistics • Integrative book chapters (Sionti, Ai, Rosé, & Resnick, in press; Howley, Mayfield, & Rosé, in press)

19. Learning Results • Students in accountable talk classrooms (where transactivity is elicited) learn more than students in a traditional classroom (O’Connor, Michaels, & Resnick, in preparation) • Transactivity correlates with learning (Joshi & Rosé, 2007; Kumar et al., 2007) • Consistent with results in connection with elaborated explanations (e.g., Webb, Nemer, Zuniga, 2002) • Collaboration support that increases transactivity increases learning (Wang et al., 2007)

20. Accountable Talk (O’Connor, Michaels, & Resnick) Eddie: Well, i don't think it matters what order the numbers are in. You still get the same answer. But three times four and four times three seem like they could be talking about different things. Teacher: Rebecca, do you agree or disagreewith what Eddie is saying? Rebecca: Well, I agree that it doesn't matter which number is first, because they both give you twelve. But I don't get what Eddie means about them saying different things. Teacher: Eddie, would you explain what you mean? Eddie: Well, I just think that like three times four can mean three groups of four things, like three bags of four apples. And four times three means four bags of three apples, and those don't seem like the same thing. Tiffany: But you still have the same number of apples, so they are the same! Teacher: OK, so we have two different ideashere to talk about. Eddie says the order does matter, because the two orders can be used to describe different situations. So Tiffany, are you sayingthat three times four and four times three can't be used to describe two different situations?

21. In vivo studies • 3 in vivo studies in 9th grade biology • Other similar studies in math, freshman engineering, thermodynamics, and chemistry • Online small group activities, support from Conversational Computer Agents Small Group Activity (Experimental Manipulation) Whole class Discussion Post-Activity test Post-Discussion test Pretest Example Intervention: Revoicing Agent

22. Teacher: Accountable TalkComparing Years 1 and 2 Auto Predicted AT R = .45 R = .36

23. Student talkComparing Years 1 and 2 Average Student Words per Turn R = .59 R = .18

24. Teacher: Accountable TalkComparing Years 1 and 2 Auto Predicted AT Auto Predicted AT Regression Scatter Plot R = .45 R = .36

25. Student talkComparing Years 1 and 2 Regression Scatter Plot Average Student Words per Turn Average Student Words per Turn R = .59 R = .18

26. Current Directions • Continuing to investigate social considerations for integrating dialogue agents with groups • Investigating how motivation orientation interacts with treatment • Working with groups larger than pairs (Gweon et al., in press) • Monitoring collaboration quality from speech • Challenges of multi-party conversation analysis • Multiple interwoven threads (Rosé et al., 1995; Wang et al., 2008a,b)

27. Thank You !!! Funding: The Office of Naval Research and the National Science Foundation