1. The document discusses assessing and supporting computer-supported collaborative learning (CSCL). It provides an overview of the history and goals of the CSCL field. 2. A key topic is developing methods to assess the quality of collaboration in CSCL environments. The document describes dimensions of collaboration quality and presents a rating scheme used to evaluate collaboration. 3. Supporting collaborative learning is also discussed. Collaboration scripts that provide instructions to structure interaction are described, as well as adaptive support approaches that tailor feedback based on interaction analysis. An example study of supporting collaborative inferences is presented.

1. Assessing and promoting computer-supported collaborative learning Anne Meier University of Freiburg, Institute of Psychology [email_address]

2. Introduction to CSCL (computer-supported collaborative learning) Assessing CSCL learning processes Supporting CSCL learning processes Example study: adaptive support for knowledge co-construction Overview

3. The CSCL community a short history of CSCL… “ seeds” in the 1980s, e.g. 1989 NATO-sponsored workshop “computer-supported collaborative learning” (Maratea, Italy) since 1995: bi-annual CSCL conferences since 2003: CSCL community part of International Society of the Learning Sciences (ISLS) own journal: International Journal of CSCL (ijCSCL) published by Springer since 2006 highly interdisciplinary community

4. Introduction to CSCL CSCL researchers study: How people can learn together with the help of computers (Stahl, Koschmann, & Suthers, 2007) How technology can facilitate the sharing and creation of knowledge and expertise through peer interaction and group learning processes (Restra & Laferrière, 2007)

5. advantages/strengths challenges/pitfalls What is your experience with (computer-supported) collaborative learning?

6. Neo-Piagetian perspective learning = cognitive restructuring resolving socio-cognitive conflict arising from peer collaboration Cognitive elaboration perspective learning = elaboration and integration of knowledge very important: constructing explanations Neo-Vygotskian perspective learning = appropriation, internalization knowledge co-construction; scaffolding and fading Situated learning perspective learning = increasingly “central” participation in a community of practice distributed cognition: persons, tools, symbols, artefacts,… What makes collaborative learning effective? See for example: Cohen, 1994; Dillenbourg et al., 1995; Fischer, 2002; Webb & Palincsar, 1996)

7. Motivational process loss (e.g. Salomon & Globerson, 1989) Free-rider effect (“social loafing”) Sucker effect Production blocking having to wait for others to finish their turn e.g. in brainstorming (Diehl & Stroebe, 1987) Biased information sampling (e.g. Brodbeck et al., 2007; Stasser & Titus, 1985)) neglecting individuals’ unique knowledge striving for consensus rather than understanding  Putting people in a (computer-supported) group does not mean that they will collaborate well! Pitfalls of collaborative learning

8. Introduction to CSCL (computer-supported collaborative learning) Assessing CSCL learning processes  What characterizes “good” computer-supported collaborative learning? Supporting CSCL learning processes Example study: adaptive support for knowledge co-construction Overview

9. Cognitive, social, and affective aspects of collaboration quality in CSCL Communication (Clark & Brennan, 1991) Grounding adapting utterances to the amount of shared knowledge/ perspective/ experience establishing referential identity (e.g. of objects in a shared whiteboard, of previous messages/ contributions) establishing a shared terminology Conversation management initiating conversations managing turn-taking ensuring that contributions are taken up For additional literature/ references, please see Meier, Spada, & Rummel, 2007

10. Cognitive, social, and affective aspects of collaboration quality in CSCL Joint information-processing Elaborative information-processing eliciting and providing elaborated explanations using the partner as a resource elaborating on partners’ contributions Argumentative information-processing constructing justified arguments and counterarguments engaging in a critical discussion: avoiding an illusion of consensus

11. Cognitive, social, and affective aspects of collaboration quality in CSCL Coordination (explicit or tacit) (e.g. Malone & Crowstone, 1994) Task division identifying interdependent subtasks blending individual and collaborative work Time management agreeing on a realistic time schedule monitoring the remaining time during the work process Resource management handling the available tools efficiently agreeing on who may use a technical feature at what time

12. Cognitive, social, and affective aspects of collaboration quality in CSCL Relationship management maintaining equal participation symmetric or complementary, depending on role structure solving conflicts constructively epistemic vs. social conflicts

13. Cognitive, social, and affective aspects of collaboration quality in CSCL Motivation individual task orientation keeping up a high level of expended effort volitional processes: focusing attention, exerting motivation control mutual self-regulation mutual encouragement monitoring performance and giving feedback

14. Example: Collaboration quality rating-scheme Development sample from study on interdisciplinary collaboration : students of psychology and medicine solving complex patient cases (Rummel & Spada, 2005) Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2 , 63-86.

15. Example: Collaboration quality rating-scheme Control Room Experimental Room I Experimental Room II

16. Example: Collaboration quality rating-scheme Development sample from study on interdisciplinary collaboration : students of psychology and medicine solving complex patient case (Rummel & Spada, 2005) data- and theory-driven analyses  5 aspects/ 9 dimensions for each dimension: collaboration “standard” defined and illustrated in rating handbook collaboration quality rated on 5-point scales

17. Example: Collaboration quality rating-scheme model / script > control model-plus > model Information pooling Task division Time management Technical coordination model > control > script model-plus > model Individual task orientation Quality of joint solution (Rummel, Spada, & Hauser, 2009) Rummel rummele

18. Example: Collaboration quality rating-scheme adaptation to new CSCL setting (Synergo) (Voyiatzaki et al., 2008) descriptive framework valid in this setting as well But: changed operationalization of dimensions and re-anchoring of scales necessary … .. work in progress: providing adaptive feedback to students based on ratings of their collaboration quality (Meier, Voyiatzaki, Kahrimanis, Rummel, Spada, Avouris, 2008)

19. Introduction to CSCL (computer-supported collaborative learning) Assessing CSCL learning processes Supporting CSCL learning processes  How can beneficial collaboration processes be facilitated? Example study: adaptive support for knowledge co-construction Overview

20. Supporting CSCL learning processes Earlier approaches: support “around” collaboration Collaboration scripts: support during collaboration Adaptivity: from fixed to flexible support

21. Supporting CSCL learning processes Earlier approaches: support “around” collaboration Support prior to collaboration, e.g. training for strategic questioning (King, 1991) Support after collaboration, e.g. group processing approaches (Yager, Johnson, Johnson, & Snider, 1986) Collaboration scripts: support during collaboration Adaptivity: from fixed to flexible support after: Diziol & Rummel, accepted

22. Supporting CSCL learning processes Earlier approaches: support “around” collaboration Collaboration scripts: support during collaboration provide specific instructions about task-related interaction (Kollar et al., 2006) Sequencing work phases Distributing roles Specifying activities  goal: enhance cognitive, meta-cognitive and social learning processes Adaptivity: from fixed to flexible support after: Diziol & Rummel, accepted

23. Collaboration Scripts S plit W here I nteraction S hould H appen (SWISH) ( Dillenbourg & Jermann, 2007) Schema Split Compensation mutual regulation cognitive & metacognitive tasks (e.g. recaller & detector) Reciprocal argumentation conflicting opinions (e.g. pro & contra-roles) Conflict exchange of information distribution of knowledge (e.g. expert groups & teams) Jigsaw

24. Supporting CSCL learning processes Earlier approaches: support “around” collaboration Collaboration scripts: support during collaboration Adaptivity: from fixed scripts to flexible support Danger of “overscripting” collaboration (Dillenbourg, 2002); instead: taking into account students’ prior knowledge and “internal collaboration scripts” realizing flexible, adaptive support: “ Wizard of Oz” studies adaptive feedback based on automated analyses of interaction (e.g. Dönmez et al, 2005) after: Diziol & Rummel, accepted

25. Introduction to CSCL (computer-supported collaborative learning) Assessing CSCL learning processes Supporting CSCL learning processes Example study: adaptive support for knowledge co-construction Overview

26. Example: Supporting Collaborative Inferences F - I - R - E ! Figure from: Bauer, K., & Hesse, F. (2006). Von Kopf zu Kopf. [From head to head]. Gerhirn und Geist [Brain & Mind], 5/2006, 34-39.

27. Example: Supporting Collaborative Inferences Wolfgang‘s fingerprints are on the gun. Wolfgang showed the guns to his guests in the afternoon. A B

28. Example: Supporting Collaborative Inferences Wolfgang‘s fingerprints are on the gun. Wolfgang showed the guns to his guests in the afternoon. A B

29. Example: Supporting Collaborative Inferences Wolfgang left his fingerprints on the weapon when he showed it to his guests. A B

30. Example: Supporting Collaborative Inferences Meier & Spada, 2007 Person B Person A shared individual collaborative Inference type Information distribution ** *

31. Example: Supporting Collaborative Inferences Why is it so difficult to draw collaborative inferences? individual group member holds “unconnected” information seen as less relevant and therefore less likely to be brought up during discussion (Fraidin, 2004) inference must be drawn on the basis of newly learned information and recalled information people tend to focus on old rather than new information (Wittenbaum, Hubbel & Zuckermann, 1999) recall vulnerable to disruptions in group discussion (Finlay, Hitch & Meudell, 2000) Training Experiment: train collaboration strategies for drawing inferences pooling “unconnected” information taking up new information

32. Example: Supporting Collaborative Inferences Meier & Spada, 2008 Test task (murder mystery) No Training (n=9 ) Testing phase collaborative reflection ... with inference tutoring tool discussion & solution Training task (medical diagnosis): individual reading phase read text on collaboration strategies Training phase Training Task + Text + Tutoring (n=9) Training Task+ Text (n=9) Training Task (n=9)

33. Example: Supporting Collaborative Inferences 6 7 5 8 4 2 ... 3 1 inference patient information disease information New Information! ANJA has matching information.

34. Example: Supporting Collaborative Inferences 6 7 5 8 4 2 ... 3 1 inference patient information disease information Well done! You have drawn an important inference!

35. Example: Supporting Collaborative Inferences Meier & Spada, 2008 Test task (murder mystery) No Training (n=9 ) Testing phase collaborative reflection ... with inference tutoring tool discussion & solution Training task (medical diagnosis): individual reading phase read text on collaboration strategies Training phase Training Task + Text + Tutoring (n=9) Training Task+ Text (n=9) Training Task (n=9)

36. Example: Supporting Collaborative Inferences ** ** performance during testing (without tutoring tool) Meier & Spada, 2008

37. In a nutshell… Introduction to CSCL (computer-supported collaborative learning) diverse perspectives on collaborative learning within field of CSCL successful collaboration does not occur spontaneously! Assessing CSCL learning processes focus here was on processes, rather than outcomes or preconditions many relevant aspects: communication, information-processing, coordination, relationship management, motivation Supporting CSCL learning processes collaboration scripts: (computer-)support during collaboration moving towards more flexible, more adaptive support Example study: adaptive support for knowledge co-construction collaborative inferences are important but difficult adaptive support yields best training results

38. Many thanks to the CoEmCo-Team: Hans Spada, Nikol Rummel Dejana Diziol, Sabine Hauser Eva Zerpies, Malte Jansen This work was funded by Thank you for your attention!

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40. Example: Collaboration quality rating-scheme Meier, A., Spada, H. & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2 , 63-86. Time management Information pooling Joint information processing Reaching consensus Sustaining mutual understanding Communication Task division Coordination Technical coordination Individual task orientation Motivation Reciprocal interaction Relationship management Dialog management

41. Outcomes of collaborative learning When are groups better than individuals? insights from social psychology (Kraut, 2003) Aggregation: combining the unique resources of individual group members Making use of members’ complementary knowledge, perspectives, skills etc. e.g. a cross-functional marketing team making strategic decisions based on members’ complementary expertise Synergy: going beyond the resources contributed by group members building on each others’ contributions, creating innovative ideas & solutions e.g. a product-design team developing a new product “ assembly bonus” however: groups tend to neglect members’ unique knowledge and focus instead on shared knowledge (Stasser & Titus, 1985)

42. Measuring the success of computer-supported collaborative learning Individual learning types of knowledge and skills conceptual vs. procedural skills: domain, collaboration, self-regulation, computer-literacy level of evaluation: subjective evaluation - retention – transfer Group-level learning transactive memory, shared mental models  facilitates future collaboration in the same group Interpersonal and motivational outcomes trust, liking, friendships,… self- and group-efficacy interest  motivation for future collaboration

43. Measuring the success of computer-supported collaborative learning What characterizes “success” in the TEL-environment you study? How do you assess it?

44. Assessing collaboration quality cognitive, social, and affective processes process gain: assembly bonus, synergy effects process loss: motivation and coordination problems group size & composition task type technical & informational resources goal structure Group learning, e.g. transactive memory, shared mental models Individual learning - domain knowledge and problem-solving skills - collaboration skills - technical skills Interpersonal & motivational outcomes, e.g. trust, liking, self-efficacy, group-efficacy institutional context Input