Learning to collaborate while being scripted or by observing a model



In an earlier study, we had tested if observing a collaboration model, or alternatively, following a collaboration script could improve students’ subsequent collaboration in a computer-mediated setting and promote their knowledge of good collaboration. Both model and script showed positive effects. The current study was designed to further probe the effects of model and script by comparing them to conditions in which the learning was supported by providing elaboration support (instructional prompts and a reflective self-explanation phase). In addition, we applied a newly developed, innovative rating scheme to analyze the collaborative process: The rating scheme combines qualitative evaluation with quantitative assessment. Forty dyads were tested, eight in each of the following conditions: model plus elaboration, model, script plus elaboration, script, and control. Observing a collaboration model with elaboration support yielded the best results over all other conditions on measures of the quality of collaborative process and on outcome variables. Model without elaboration was second best. The results for the script conditions were mixed; on some variables, even below those of the control condition. The results of the current study lead us to challenge the positive view on collaboration scripts prevalent in CSCL research. We propose adaptive scripting as a possible solution.


Computer-mediated collaboration Collaboration script Elaboration support Observational learning Worked-out collaboration example 



The present research was supported by the Deutsche Forschungsgemeinschaft (DFG; [German Science Foundation]) with project grants to Hans Spada and Franz Caspar (Sp 251/16-2 and 16-3). We would like to thank our student research assistants Dejana Diziol, Jana Groß Ophoff, Cindy Günzler, and Friederike Renner for their help in the material development, data collection, and data analysis. Furthermore, we would like to acknowledge Anne Meier, who has made a substantial contribution to this project: The rating scheme for the collaborative process analysis was largely developed as part of her thesis work.


  1. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  2. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  3. Barron, B. (2000). Achieving coordination in collaborative problem-solving groups. Journal of the Learning Sciences, 9, 403–436.CrossRefGoogle Scholar
  4. Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
  5. Barrows, H. S. (1986). A taxonomy of problem-based learning methods. Medical Education, 20, 481–486.CrossRefGoogle Scholar
  6. Bauer, M. (1999). Modellierungsmethoden in der Verhaltenstherapie. Eine kritische Analyse des Modell-Konzepts und der zugehörigen Forschung. [Modelling methods in behavior therapy. A critical analysis of the modeling concept and of related research.]. Regensburg, Germany: S. Roderer Verlag.Google Scholar
  7. Bielaczyc, K., Pirolli, P., & Brown, A. L. (1994). Collaborative explanations and metacognition: Identifying successful learning activities in the acquisition of cognitive skills. In A. Ram, & K. Eiselt (Eds.), Proceedings of the sixteenth annual cognitive science society conference (pp. 39–44). Hillsdale, NJ: Erlbaum.Google Scholar
  8. Bransford, J., Brown, A., & Cocking, R. (Eds.) (2000). How people learn: Brain, mind, experience and school. Washington: National Academy.Google Scholar
  9. Cameron, T., Barrows, H. S., & Crooks, S. M. (1999). Distributed problem-based learning at Southern Illinois University school of medicine. In C. Hoadley, & J. Roschelle (Eds.), Computer support for collaborative learning. Designing new media for a new millennium: Collaborative technology for learning, education, and training (pp. 86–94). Palo Alto, CA: Stanford University.Google Scholar
  10. Caspar, F. (1997). What goes on in a psychotherapist’s mind? Psychotherapy Research, 7, 105–125.Google Scholar
  11. Chi, M. T. H. (2000). Self-explaining expository text: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (pp. 161–238). Mahwah, NJ: Erlbaum.Google Scholar
  12. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.CrossRefGoogle Scholar
  13. Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.CrossRefGoogle Scholar
  14. Chi, M. T. H., Roy, M., & Hausmann, R. G. M. (2008). Observing tutorial dialogues collaboratively: insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301–341.CrossRefGoogle Scholar
  15. Clark, H. H. (1996). Using language. Cambridge MA: Cambridge University Press.Google Scholar
  16. Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–148). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  17. Clark, H. H., & Murphy, G. L. (1982). Audience design in meaning and reference. In J. F. LeNy, & W. Kintsch (Eds.), Language and comprehension (pp. 287–299). Amsterdam, NL: North-Holland.Google Scholar
  18. Cox, R., McKendree, J., Tobin, R., Lee, J., & Mayes, T. (1999). Vicarious learning from dialogue and discourse. Instructional Science, 27, 431–458.Google Scholar
  19. Craik, F., & Lockhart, R. (1972). Levels of processing: a framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684.CrossRefGoogle Scholar
  20. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum.Google Scholar
  21. Decker, P. J. (1980). Effects of symbolic coding and rehearsal in behavior-modeling training. Journal of Applied Psychology, 65, 627–634.CrossRefGoogle Scholar
  22. Decker, P. J. (1984). Effects of different symbolic coding stimuli in behavior modeling training. Personnel Psychology, 37(4), 711–720.CrossRefGoogle Scholar
  23. Decker, P. J., & Nathan, B. R. (1985). Behavior modeling training. Principles and applications. New York, NY: Praeger.Google Scholar
  24. Dillenbourg, P. (1999). Introduction: What do you mean by “collaborative learning”? In P. Dillenbourg (Ed.), Collaborative learning. Cognitive and computational approaches (pp. 1–19). Amsterdam, NL: Pergamon.Google Scholar
  25. Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL (pp. 61–91). Heerlen, NL: Open Universiteit Nederland.Google Scholar
  26. Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1995). The evolution of research on collaborative learning. In P. Reimann, & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp. 189–211). Oxford, UK: Elsevier/Pergamon.Google Scholar
  27. Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3, 5–23.CrossRefGoogle Scholar
  28. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for computer-supported collaborative learning. Journal of Computer Assisted Learning, 23(1), 1–13.CrossRefGoogle Scholar
  29. Ferguson-Hessler, M. G. M., & de Jong, T. (1990). Studying physics text; differences in study processes between good and poor performers. Cognition and Instruction, 7, 41–75.CrossRefGoogle Scholar
  30. Finn, K. E., Sellen, A. J., & Wilbur, S. B. E. (1997). Video-mediated communication. Mahwah, NJ: Erlbaum.Google Scholar
  31. Fischer, F., & Mandl, H. (2003). Being there or being where? Videoconferencing and cooperative learning. In H. van Oostendorp (Ed.), Cognition in a digital world (pp. 205–223). Mahwah, NJ: Erlbaum.Google Scholar
  32. Fischer, F., Kollar, I., Mandl, H., & Haake, J. (Eds.). (2007). Scripting computer-supported communication of knowledge. Cognitive, computational, and educational perspectives. New York, NY: Springer.Google Scholar
  33. Greeno, J. G., & the Middle School Mathematics through Applications Project Group (1998). The situativity of knowing, learning and research. American Psychologist, 53, 5–26.CrossRefGoogle Scholar
  34. Gweon, G., Rosé, C., Carey, R., & Zaiss, Z. (2006). Providing support for adaptive scripting in an on-line collaborative learning environment. In Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems (pp. 251–260). ACM.Google Scholar
  35. Heckhausen, H. (1989). Motivation und Handeln. Berlin, Germany: Springer.Google Scholar
  36. Hermann, F., Rummel, N., & Spada, H. (2001). Solving the case together: The challenge of net-based interdisciplinary collaboration. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), Proceedings of the first European conference on computer-supported collaborative learning (pp. 293–300). Maastricht, NL: McLuhan Institute.Google Scholar
  37. Hilbert, T., Schworm, S., & Renkl, A. (2004). Learning from worked-out examples: The transition from instructional explanations to self-explanation prompts. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional design for effective and enjoyable computer-supported learning (pp. 184–192). Tübingen, Germany: Knowledge Media Research Center.Google Scholar
  38. Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997). The emerging conceptualization of groups as information processors. Psychological Bulletin, 121(1), 43–64.CrossRefGoogle Scholar
  39. Johnson, D. W., & Johnson, R. T. (1992). Key to effective cooperation. In R. Hertz-Lazarowitz, & N. Miller (Eds.), Interaction in cooperative groups. The theoretical anatomy of group learning (pp. 174–199). New York, NY: Cambridge University Press.Google Scholar
  40. Johnson, D. W., & Johnson, R. T. (2003). Training for cooperative group work. In M. A. West, D. Tjosvold, & K. G. Smith (Eds.), International handbook of organizational teamwork and cooperative working (pp. 167–183). Chichester, UK: Wiley.CrossRefGoogle Scholar
  41. Jucks, R., Bromme, R., & Runde, A. (2003). Audience Design von Experten in der netzgestützten Kommunikation: Die Rolle von Heuristiken über das geteilte Vorwissen. [Audience design of experts in net-based communication: the role of heuristics about shared knowledge]. Zeitschrift für Psychologie, 211(2), 60–74.CrossRefGoogle Scholar
  42. Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic data analysis. New York, NY: Guilford.Google Scholar
  43. King, A. (1991). Effects of training in strategic questioning on children’s problem-solving performance. Journal of Educational Psychology, 83(3), 307–317.CrossRefGoogle Scholar
  44. Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., Häkkinen, P., et al. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2, 211–224.CrossRefGoogle Scholar
  45. Köhler, T., & Trimpop, R. (2004). Sehen und gesehen werden: Teleradiologie mittels Desktop- Videoconferencing. [Seeing and being seen: Teleradiology by means of desktop videoconferencing.]. In W. Bungard, B. Koop, & C. Liebig (Eds.), Proceedings zur 3. Tagung der Fachgruppe Arbeits- und Organisationspsychologie. München, Germany: Rainer Hampp.Google Scholar
  46. Kramarski, B. (2004). Making sense of graphs: does metacognitive instruction make a difference on students’ mathematical conceptions and alternative conceptions? Learning and Instruction, 14(6), 593–619.CrossRefGoogle Scholar
  47. Larson, J. R., & Christensen, C. (1993). Groups as problem-solving units: toward a new meaning of social cognition. British Journal of Social Psychology, 32, 5–30.Google Scholar
  48. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.Google Scholar
  49. Malone, T. W., & Crowston, K. (1990). What is coordination theory and how can it help design cooperative work systems? Proceedings of the conference on computer-supported cooperative work (pp. 357–370). Los Angeles, CA.Google Scholar
  50. Malone, T. W., & Crowston, K. (1994). The interdisciplinary study of coordination. ACM Computing Surveys, 26(1), 87–119.CrossRefGoogle Scholar
  51. McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: a confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60, 48–58.Google Scholar
  52. Meier, A., Spada, H., & Rummel, N. (2007). Evaluating collaboration: a rating scheme for assessing the quality of collaborative process. International Journal of Computer-Supported Collaborative Learning, 2, 63–86.CrossRefGoogle Scholar
  53. Moreland, R. L., & Myaskovsky, L. (2000). Exploring the performance benefits of group training: Transactive memory or improved communication? Organizational Behavior and Human Decision Processes, 82(1), 117–133.CrossRefGoogle Scholar
  54. Nickerson, R. S. (1999). How we know—and sometimes misjudge—what others know: Imputing one’s own knowledge to others. Psychological Bulletin, 125(6), 737–759.CrossRefGoogle Scholar
  55. O’Conaill, B., & Whittaker, S. (1997). Characterizing, predicting, and measuring video-mediated communication: A conversational approach. In K. E. Finn, A. J. Sellen, & S. B. Wilbur (Eds.), Video-mediated communication (pp. 107–132). Mahwah, NJ: Erlbaum.Google Scholar
  56. O’Donnell, A. M. (1999). Structuring dyadic interaction through scripted cooperation. In A. M. O’Donnell, & A. King (Eds.), Cognitive perspectives on peer learning (pp. 179–196). Mahwah, NJ: Erlbaum.Google Scholar
  57. O’Donnell, A. M., & Dansereau, D. F. (1992). Scripted cooperation in student dyads: A method for analyzing and enhancing academic learning and performance. In R. Hertz-Lazarowitz, & N. Miller (Eds.), Interaction in cooperative groups. The theoretical anatomy of group learning (pp. 120–141). New York, NY: Cambridge University Press.Google Scholar
  58. Reimann, P. (1997). Lernprozesse beim Wissenserwerb aus Beispielen: Analyse, Modellierung, Förderung. [Processes of learning from examples: Analysis, modeling, support]. Bern, Switzerland: Huber.Google Scholar
  59. Renkl, A. (1997). Learning from worked-out examples: a study on individual differences. Cognitive Science, 21, 1–29.CrossRefGoogle Scholar
  60. Renkl, A. (2002). Learning from worked-out examples: instructional explanations supplement self-explanations. Learning & Instruction, 12, 529–556.CrossRefGoogle Scholar
  61. Renkl, A. (2005). The worked-out-example principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 229–246). Cambridge, UK: Cambridge University Press.Google Scholar
  62. Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: the effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23, 90–108.CrossRefGoogle Scholar
  63. Rummel, N., & Spada, H. (2005a). Instructional support for collaboration in desktop videoconferencing settings. How it can be achieved and assessed. In R. Bromme, F. W. Hesse, & H. Spada (Eds.), Barriers and biases in computer-mediated knowledge communication—and how they may be overcome (pp. 59–88). New York, NY: Springer.CrossRefGoogle Scholar
  64. Rummel, N., & Spada, H. (2005b). Learning to collaborate: an instructional approach to promoting collaborative problem solving in computer-mediated settings. Journal of the Learning Sciences, 14(2), 201–241.CrossRefGoogle Scholar
  65. Rummel, N., & Spada, H. (2007). Can people learn computer-mediated collaboration by following a script? In F. Fischer, I. Kollar, H. Mandl, & J. Haake (Eds.), Scripting computer-supported communication of knowledge. Cognitive, computational, and educational perspectives (pp. 47–63). New York, NY: Springer.Google Scholar
  66. Rummel, N., & Weinberger, A. (2008). New challenges in CSCL: Towards adaptive script support. In G. Kanselaar, V. Jonker, P.A. Kirschner, & F. Prins, (Eds.), International perspectives of the learning sciences: Cre8ing a learning world. Proceedings of the Eighth International Conference of the Learning Sciences (ICLS 2008 (pp. 338–345). Utrecht, NL.Google Scholar
  67. Sacks, H., Schegloff, E., & Jefferson, G. (1974). A simplest systematic for the organization of turn-taking in conversation. Language, 50, 696–753.CrossRefGoogle Scholar
  68. Slavin, R. E. (1992). When and why does cooperative learning increase achievement? Theoretical and empirical perspectives. In R. Hertz-Lazarowitz, & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 145–173). New York, NY: Cambridge University Press.Google Scholar
  69. Soller, A., Jermann, P., Muehlenbrock, M., & Martinez, A. (2005). From mirroring to guiding: a review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261–290.Google Scholar
  70. Stenning, K., McKendree, J., Lee, J., Cox, R., Dineen, F., & Mayes, T. (1999). Vicarious learning from educational dialogue. In C. M. Hoadley, & J. Roschelle (Eds.), Computer support for collaborative learning: Designing new media for a new millennium. Proceedings of CSCL 1999 (pp. 341–347). Palo Alto, CA: Stanford University.Google Scholar
  71. Stasser, G., & Titus, W. (1985). Pooling of unshared information in group decision making: Biased information sampling during group discussion. Journal of Personality and Social Psychology, 48, 1467–1478.CrossRefGoogle Scholar
  72. Stasser, G., Stewart, D., & Wittenbaum, G. (1995). Expert roles and information exchange during discussion: the importance of knowing who knows what. Journal of Experimental Social Psychology, 31, 244–265.CrossRefGoogle Scholar
  73. Strijbos, J. -W., Martens, R. L., Prins, F. J., & Jochems, W. M. G. (2006). Content analysis: what are they talking about? Computer & Education, 46, 29–48.CrossRefGoogle Scholar
  74. Tindale, R. S., Kameda, T., & Hinsz, V. B. (2003). Group decision making. In M. A. Hogg, & J. Cooper (Eds.), Sage handbook of social psychology (pp. 381–403). London, UK: Sage.Google Scholar
  75. Tsigilis, N., & Theodosiou, A. (2003). Temporal stability of the intrinsic motivation inventory. Perceptual and Motor Skills, 97, 271–280.Google Scholar
  76. VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. Posner (Ed.), Foundations of cognitive science (pp. 527–579). Cambridge, MA: MIT.Google Scholar
  77. VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539.CrossRefGoogle Scholar
  78. Walker, E., Rummel, N., & Koedinger, K. (2008). To tutor the tutor: Adaptive domain support for peer tutoring. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th international conference on intelligent tutoring systems, lecture notes in computer science, 5091 (pp. 626–635). Berlin, Germany: Springer.Google Scholar
  79. Webb, N. M. (1989). Peer interaction and learning in small groups. International Journal of Education Research, 13, 21–39.CrossRefGoogle Scholar
  80. Wecker, C., & Fischer, F. (2007). Fading scripts in computer-supported collaborative learning: The role of distributed monitoring. In C. A. Chinn, G. Erkens, & S. Puntambekar (Eds.), The proceedings of CSCL 2007: Of mice, minds and society (pp. 763–771). New Brunswick, NJ, USA.Google Scholar
  81. Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In B. Mullen, & G. R. Goethals (Eds.), Theories of group behavior (pp. 185–208). Berlin Heidelberg, New York: Springer.Google Scholar
  82. Wittenbaum, G. M., Vaughan, S. I., & Stasser, G. (1998). Coordination in task performing groups. In R. S. Tindale, et al. (Ed.),Theory and research on small groups (pp. 177–204). New York, NY: Plenum.Google Scholar
  83. World Health Organisation. (1993). Tenth revision of the international classification of diseases, Chapter V (F): Mental and Behavioural Disorders. Diagnostic Criteria for Research. World Health Organisation.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of PsychologyAlbert-Ludwigs-Universität FreiburgFreiburgGermany

Personalised recommendations