Educational Psychology Review

, Volume 27, Issue 3, pp 505–536 | Cite as

Teacher Competencies for the Implementation of Collaborative Learning in the Classroom: a Framework and Research Review

  • Celia KaendlerEmail author
  • Michael Wiedmann
  • Nikol Rummel
  • Hans Spada
Review Article


This article describes teacher competencies for implementing collaborative learning in the classroom. Research has shown that the effectiveness of collaborative learning largely depends on the quality of student interaction. We therefore focus on what a teacher can do to foster student interaction. First, we present a framework that draws a comprehensive picture of a teacher role we see as germane to fostering student interaction. The framework distinguishes between five teacher competencies that span across all implementation phases of collaborative learning: the ability to plan student interaction, monitor, support, and consolidate this interaction, and finally reflect upon it. Then, we review research on collaborative learning and structure this review along the five teacher competencies presented in the framework. The review targets relevant concepts and pivotal empirical research results about how to foster student interaction. For each competency, we first summarize relevant concepts and empirical results. We then apply the concepts and findings to a classroom situation. These teaching vignettes illustrate the functions of the five teacher competencies in fostering student interaction in collaborative learning. For each vignette, we discuss and highlight specific aspects of the presented teacher role and draw practical implications. Monitoring and supporting in the classroom should be trained in teacher education and facilitated by providing teachers with tools such as a checklist of beneficial student behaviors. These practical implications can inform educational practices and offer new directions for future research regarding promoting collaborative learning.


Teacher role Teacher competencies Collaborative learning Student interaction Literature review 



The research reported in this article was supported by the Graduate School Pro|Mat|Nat (Educational Professionalism in Mathematics and Natural Sciences). Pro|Mat|Nat is a project of the Competence Network Empirical Research in Education and Teaching (KeBU) of the University of Freiburg and the University of Education, Freiburg. The Graduate School is funded by the federal state of Baden-Wuerttemberg, Germany. The authors would like to thank Felicitas Biwer and Magdalena Wischnewski for their help with the literature search. The authors also thank Lara Burt for proofreading the article and Timo Leuders for discussions on this project.

Ethical Standards

The manuscript does not contain clinical studies or patient data.


  1. Altrichter, H., & Posch, P. (1998). Lehrer erforschen ihren Unterricht: Eine Einführung in die Methoden der Aktionsforschung [Teachers investigate their own teaching: An introduction to the methods of action research] (3rd ed.). Bad Heilbrunn: Julius Klinkhardt.Google Scholar
  2. Aronson, E., Blaney, N., Stephan, C., Sikes, J., & Snapp, M. (1978). The jigsaw classroom. Beverly Hills: Sage.Google Scholar
  3. Artzt, A. F., & Armour-Thomas, E. (1998). Mathematics teaching as problem solving: a framework for studying teacher metacognition underlying instructional practice in mathematics. Instructional Science, 26(1/2), 5–25. doi: 10.1023/A:1003083812378.Google Scholar
  4. Arvaja, M., Häkkinen, P., Eteläpelto, A., & Rasku-Puttonen, H. (2000). Collaborative processes during report writing of a science learning project: the nature of discourse as a function of task requirements. European Journal of Psychology of Education, 15(4), 455–466.Google Scholar
  5. Baer, M., Kocher, M., Wyss, C., Guldimann, T., Larcher, S., & Dörr, G. (2011). Lehrerbildung und Praxiserfahrung im ersten Berufsjahr und ihre Wirkung auf die Unterrichtskompetenzen von Studierenden und jungen Lehrpersonen im Berufseinstieg [Teacher education and practical experience in the first year of service and their effect on teaching competencies of students and young teachers at the beginning of their career]. Zeitschrift für Erziehungswissenschaft, 14(1), 85–117.Google Scholar
  6. Baker, M., Andriessen, J., Lund, K., van Amelsvoort, M., & Quignard, M. (2007). Rainbow: a framework for analyzing computer-mediated pedagogical debates. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 247–272.Google Scholar
  7. Bannert, M. (2003). Effekte metakognitiver Lernhilfen auf den Wissenserwerb in vernetzten Lernumgebungen [Effects of metacognitive learning aids on knowledge gain in computer-supported collaborative learning environments]. Zeitschrift für Pädagogische Psychologie, 17(1), 13–25.Google Scholar
  8. Bromme, R. (1992). Der Lehrer als Experte: Zur Psychologie des professionellen Wissens [The teacher as expert: on the psychology of professional knowledge] (1st ed.). Bern: Huber (Huber-Psychologie-Forschung).Google Scholar
  9. Brooks, L. W., & Dansereau, D. F. (1983). Effects of structural schema training and text organization on expository prose processing. Journal of Educational Psychology, 75, 811–820.Google Scholar
  10. Brophy, J. (2000). Teaching (educational practices series, vol. 1). Brussels: International Academy of Education (IAE). Retrieved from Scholar
  11. Brush, T. (1998). Embedding cooperative learning into the design of integrated learning systems: rationale and guidelines. Educational Technology Research and Development, 46(3), 5–18.Google Scholar
  12. Chiu, C.-H. (2004). A framework for a computer system to support distributed cooperative learning. AACE Journal, 12(1), 9–26.Google Scholar
  13. Choppin, J. M. (2007). Teacher-orchestrated classroom arguments. Mathematics Teacher, 101(4), 306–310.Google Scholar
  14. Cohen, E. G. (1994). Restructuring the classroom: conditions for productive small groups. Review of Educational Research, 64(1), 1–35.Google Scholar
  15. Colton, A. M., & Sparks-Langer, G. M. (1993). A conceptual framework to guide the development of teacher reflection and decision making. Journal of Teacher Education, 44, 45–54.Google Scholar
  16. Daudelin, M. W. (1996). Learning from experience through reflection. Organizational Dynamics, 24, 36–48.Google Scholar
  17. De Lièvre, B., Depover, C., & Dillenbourg, P. (2006). The relationship between tutoring mode and learners’ use of help tools in distance education. Instructional Science, 34, 97–129.Google Scholar
  18. Deiglmayr, A., & Spada, H. (2010). Developing adaptive collaboration support: the example of an effective training for collaborative inferences. Educational Psychology Review, 22(1), 103–113.Google Scholar
  19. Dillenbourg, P. (1999). What do you mean by ‘collaborative learning’? In P. Dillenbourg (Ed.), Collaborative learning. Cognitive and computational approaches (pp. 1–19). Oxford: Elsevier.Google Scholar
  20. Dillenbourg, P. (2002). Over-scripting CSCL. In P. A. Kirschner (Ed.), Three worlds of CSCL: can we support CSCL? (pp. 61–91). Heerlen: Open University of the Netherlands.Google Scholar
  21. Dillenbourg, P., & Crivelli, Z. (2009). A model of collaborative learning scripts instantiated with mobile technologies. International Journal of Mobile and Blended Learning, 1(1), 36–48.Google Scholar
  22. Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer Supported Collaborative Learning, 3(1), 5–23.Google Scholar
  23. Dillenbourg, P., & Jermann, P. (2006). Designing integrative scripts. In F. Fischer, H. Mandl, J. Haake, & I. Kollar (Eds.), Scripting computer-supported collaborative learning: cognitive, computational and educational perspectives (pp. 275–301). New York: Springer.Google Scholar
  24. Dillenbourg, P., & Jermann, P. (2010). Technology for classroom orchestration. In M. S. Khine & I. M. Saleh (Eds.), New science of learning: cognition, computers and collaboration in education (pp. 525–552). New York: Springer.Google Scholar
  25. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for CSCL. Journal of Computer Assisted Learning, 23(1), 1–13.Google Scholar
  26. Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1996). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines. Towards an interdisciplinary learning science (1st ed., pp. 189–211). Oxford: Pergamon.Google Scholar
  27. Dillenbourg, P., Zufferey, G., Alavi, H., Jermann, P., Do-Lenh, S., Bonnard, Q., … Kaplan, F. (2011). Classroom orchestration: the third circle of usability. In H. Spada, G. Stahl, N. Miyake, & N. Law (Eds.), Connecting computer-supported collaborative learning to policy and practice: CSCL2011 Conference Proceedings. Volume Ilong papers (pp. 510–517). International Society of the Learning Sciences.Google Scholar
  28. Diziol, D., & Rummel, N. (2010). How to design support for collaborative e-learning: a framework of relevant dimensions. In B. Ertl (Ed.), E-collaborative knowledge construction: learning from computer-supported and virtual environments (pp. 162–179). Hershey: IGI Global.Google Scholar
  29. Diziol, D., Walker, E., Rummel, N., & Koedinger, K. R. (2010). Using intelligent tutor technology to implement adaptive support for student collaboration. Educational Psychology Review, 22(1), 89–102.Google Scholar
  30. Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206–214.Google Scholar
  31. Ertl, B., Reiserer, M., & Mandl, H. (2005). Fostering collaborative learning in videoconferencing: the influence of content schemes and collaboration scripts on collaboration outcomes and individual learning outcomes. Education, Communication & Information, 5(2), 147–166.Google Scholar
  32. Falchikov, N., & Goldfinch, J. (2000). Student peer assessment in higher education: a meta-analysis comparing peer and teacher marks. Review of Educational Research, 70(3), 287–322.Google Scholar
  33. Fischer, F., Kollar, I., & Stegmann, K. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66.Google Scholar
  34. García, M., Sánchez, V., & Escudero, I. (2006). Learning through reflection in mathematics teacher education. Educational Studies in Mathematics, 64(1), 1–17.Google Scholar
  35. Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding ill-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research & Development, 52(2), 5–22.Google Scholar
  36. Gielen, M., & de Wever, B. (2013). Structuring the PA process: impact on feedback quality. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings, volume 2short papers, panels, posters, demos, & community events (pp. 255–256). International Society of the Learning Sciences.Google Scholar
  37. Gillies, R. M., & Boyle, M. (2010). Teachers’ reflections on cooperative learning: issues of implementation. Teaching and Teacher Education, 26, 933–940.Google Scholar
  38. Gillies, R. M., Ashman, A., & Terwel, J. (Eds.). (2008). The teacher’s role in implementing cooperative learning in the classroom. New York: Springer.Google Scholar
  39. Green, N., & Green, K. (2010). Kooperatives Lernen im Klassenraum und im Kollegium. Das Trainingshandbuch (5th ed.). Seelze: Kallmeyer.Google Scholar
  40. Greif, S. (2008). Coaching und ergebnisorientierte Selbstreflexion. Göttingen: Hogrefe.Google Scholar
  41. Greiffenhagen, C. (2012). Making rounds: the routine work of the teacher during collaborative learning with computers. International Journal of Computer-Supported Collaborative Learning, 7(1), 11–42.Google Scholar
  42. Gros, B. (2001). Instructional design for computer-supported collaborative learning in primary and secondary school. Computers in Human Behavior, 17(5–6), 439–451.Google Scholar
  43. Haag, L., & Dann, H. D. (2001). Lehrerhandeln und Lehrerwissen als Bedingungen erfolgreichen Gruppenunterrichts [Teacher practice and teacher knowledge as conditions of successful collaborative learning]. Zeitschrift für Pädagogische Psychologie, 15(1), 5–15.Google Scholar
  44. Hämäläinen, R., & Häkkinen, P. (2010). Teachers’ instructional planning for computer-supported collaborative learning: macro-scripts as a pedagogical method to facilitate collaborative learning. Teaching and Teacher Education: An International Journal of Research and Studies, 26(4), 871–877.Google Scholar
  45. Hämäläinen, R., & Vähäsantanen, K. (2011). Theoretical and pedagogical perspectives on orchestrating creativity and collaborative learning. Educational Research Review, 6(3), 169–184.Google Scholar
  46. Helmke, A. (2009). Unterrichtsqualität und Lehrerprofessionalität: Diagnose, Evaluation und Verbesserung des Unterrichts [Lesson quality and teacher professionality: diagnosis, evaluation and improvement of lessons] (1st ed.). Seelze-Velber: Klett.Google Scholar
  47. Isotani, S., Mizoguchi, R., Isotani, S., Capeli, O. M., Isotani, N., de Albuquerque, A. R. P. L., … Jaques, P. (2013). A semantic web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories. Computers & Education, 63, 267–284.Google Scholar
  48. Jackson, P. W. (1968). Life in classrooms. New York: Holt, Rinehart & Winston.Google Scholar
  49. Jermann, P., & Dillenbourg, P. (2008). Group mirrors to support interaction regulation in collaborative problem solving. Computers & Education, 51, 279–296.Google Scholar
  50. Johnson, D. W., & Johnson, R. T. (1989). Cooperation and competition: theory and research. Edina: Interaction Book Company.Google Scholar
  51. Johnson, D. W., & Johnson, R. T. (1994). Structuring academic controversy. In S. Sharan (Ed.), Handbook of cooperative learning methods (pp. 66–81). Westport: Greenwood.Google Scholar
  52. Johnson, D. W., & Johnson, R. T. (1998). Cooperative learning and social interdependence theory. In R. S. Tindale, L. Heath, J. Edwards, E. J. Posavac, F. B. Bryant, & Y. Suarez-Balcazar (Eds.), Social psychological applications to social issues (Theory and research on small groups, Vol. 4, pp. 9–35). New York: Plenum.Google Scholar
  53. Johnson, D. W., Johnson, R. T., & Holubec, E. (1998a). Cooperation in the classroom. Boston: Allyn & Bacon.Google Scholar
  54. Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998b). Active learning: cooperation in the college classroom. Edina: Interaction Book Company.Google Scholar
  55. Kaendler, C., Wiedmann, M., Rummel, N., Leuders, T., & Spada, H. (2013). Transferring CSCL findings to face-to-face teacher practice. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings, volume 2short papers, panels, posters, demos & community events (pp. 279–280). International Society of the Learning Sciences.Google Scholar
  56. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. doi: 10.1207/S15326985EP3801_4.Google Scholar
  57. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.Google Scholar
  58. Kapur, M. (2010). Productive failure in mathematical problem solving. Instructional Sciences, 38(6), 523–550.Google Scholar
  59. Kapur, M. (2011). A further study of productive failure in mathematical problem solving: unpacking the design components. Instructional Sciences, 39(4), 561–579.Google Scholar
  60. Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21(1), 45–83.Google Scholar
  61. Kapur, M., & Kinzer, C. K. (2009). Productive failure in CSCL groups. Computer Supported Learning, 4(1), 21–46.Google Scholar
  62. Karakostas, A., & Demetriadis, S. (2011). Enhancing collaborative learning through dynamic forms of support: the impact of an adaptive domain-specific support strategy. Journal of Computer Assisted Learning, 27(3), 243–258.Google Scholar
  63. King, A. (2008). Structuring peer interaction to promote higher-order thinking and complex learning in cooperating groups. In R. M. Gillies, A. Ashman, & J. Terwel (Eds.), The teacher’s role in implementing cooperative learning in the classroom (pp. 73–92). New York: Springer.Google Scholar
  64. Kirschner, P., Strijbos, J.-W., Kreijns, K., & Beers, P. J. (2004). Designing electronic collaborative learning environments. Educational Technology Research and Development, 52(3), 47–66.Google Scholar
  65. Klieme, E., & Leutner, D. (2006). Kompetenzmodelle zur Erfassung individueller Lernergebnisse und zur Bilanzierung von Bildungsprozessen: Beschreibung eines neu eingerichteten Schwerpunktprogramms der DFG [Competency models for measuring individual learning and for evaluating education processes: description of a newly-established Priority Program of the DFG]. Zeitschrift für Pädagogik, 52(6), 876–903.Google Scholar
  66. Kobbe, L., Weinberger, A., & Dillenbourg, P. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224.Google Scholar
  67. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.Google Scholar
  68. Koedinger, K. R., Aleven, V., Roll, I., & Baker, R. S. J. D. (2009). In vivo experiments on whether supporting metacognition in intelligent tutoring systems yields robust learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 383–412). New York: Routledge.Google Scholar
  69. Kopp, B., & Mandl, H. (2011). Fostering argument justification using collaboration scripts and content schemes. Learning and Instruction, 21(5), 636–649.Google Scholar
  70. Korthagen, F. A. J. (1992). Techniques for stimulating reflection in teacher education seminars. Teaching and Teacher Education, 8(3), 265–274.Google Scholar
  71. Kumar, R., Rosé, C. P., Wang, Y. C., Joshi, M., & Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. In R. Luckin, K. R. Koedinger, & J. E. Greer (Eds.), Artificial intelligence in education, building technology rich learning contexts that work, Proceedings of the 13th International Conference on Artificial Intelligence in Education, AIED 2007 (pp. 383–390). Los Angeles: IOS.Google Scholar
  72. Kyndt, E., Raes, E., Lismont, B., Timmers, F., Cascallar, E., & Dochy, F. (2013). A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings? Educational Research Review, 10, 133–149.Google Scholar
  73. Lampert, M. (1990). Connecting inventions with conventions: the teachers’ role in classroom communication about mathematics. In L. Steffe & T. Wood (Eds.), Transforming early childhood mathematics education (pp. 253–265). Hillsdale: Erlbaum.Google Scholar
  74. Loibl, K., & Rummel, N. (2013). Delaying instruction alone doesn’t work: comparing and contrasting student solutions is necessary for learning from problem-solving prior to instruction. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings, volume 1Full papers & symposia (pp. 296–303). International Society of the Learning Sciences.Google Scholar
  75. Lou, Y., Abrami, P. C., Spence, J. C., Poulsen, C., Chambers, B., & d’Apollonia, S. (1996). Within-class grouping: a meta-analysis. Review of Educational Research, 66(4), 423–458. doi: 10.3102/00346543066004423.Google Scholar
  76. Lowyck, J., & Pöysä, J. (2001). Design of collaborative learning environments. Computers in Human Behavior, 17(5–6), 507–516.Google Scholar
  77. Mäkitalo, K., Weinberger, A., Häkkinen, P., Järvelä, S., & Fischer, F. (2005). Epistemic cooperation scripts in online learning environments: fostering learning by reducing uncertainty in discourse? Computers in Human Behavior, 21(4), 603–622.Google Scholar
  78. Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.Google Scholar
  79. 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(1), 63–86.Google Scholar
  80. Molenaar, I., Chiu, M. M., Sleegers, P., & van Boxtel, C. (2011). Scaffolding of small groups’ metacognitive activities with an avatar. International Journal of Computer-Supported Collaborative Learning, 6(4), 601–624.Google Scholar
  81. Morris, R., Hadwin, A. F., Gress, C. L. Z., Miller, M., Fior, M., Church, H., & Winne, P. H. (2010). Designing roles, scripts, and prompts to support CSCL in gStudy. Computers in Human Behavior, 26(5), 815–824.Google Scholar
  82. Mullins, D., Rummel, N., & Spada, H. (2011). Are two heads always better than one? Differential effects of collaboration on students’ computer-supported learning in mathematics. International Journal of Computer Supported Collaborative Learning, 6(3), 421–443.Google Scholar
  83. Oser, F., Heinzer, S., & Salzmann, P. (2010). Die Messung der Qualität von professionellen Kompetenzprofilen von Lehrpersonen mit Hilfe der Einschätzung von Filmvignetten. Chancen und Grenzen des advokatorischen Ansatzes [Measuring the quality of professional competency teaching profiles by means of evaluating film-vignettes: chances and limits of the advocatory approach]. Unterrichtswissenschaft, 38(1), 5–28.Google Scholar
  84. Palincsar, A., & Brown, A. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.Google Scholar
  85. Parnell, W. (2012). Experiences of teacher reflection: Reggio inspired practices in the studio. Journal of Early Childhood Research, 10(2), 117–133.Google Scholar
  86. Pauli, C., & Reusser, K. (2000). Zur Rolle der Lehrperson beim kooperativen Lernen [On the role of the teacher in collaborative learning]. Schweizerische Zeitschrift für Bildungswissenschaften, 22(3), 421–442.Google Scholar
  87. Persico, D., Pozzi, F., & Sarti, L. (2010). Monitoring collaborative activities in computer supported collaborative learning. Distance Education, 31(1), 5–22.Google Scholar
  88. Phielix, C., Prins, F. J., & Kirschner, P. A. (2010). Awareness of group performance in a CSCL-environment: effects of peer feedback and reflection. Computers in Human Behavior, 26(2), 151–161.Google Scholar
  89. Renkl, A. (2007). Kooperatives Lernen [Collaborative learning]. In W. Schneider & M. Hasselhorn (Eds.), Handbuch Psychologie, Bd. Pädagogische Psychologie (pp. 84–94). Göttingen: Hogrefe.Google Scholar
  90. Renkl, A., & Beisiegel, S. (2003). Lernen in Gruppen: Ein Minihandbuch [Learning in groups: a mini handbook]. Landau: Verlag Empirische Pädagogik.Google Scholar
  91. Renkl, A., Wittwer, J., Große, C., Hauser, S., Hilbert, T., Nückles, M., & Schworm, S. (2006). Instruktionale Erklärungen beim Erwerb kognitiver Fertigkeiten: Sechs Thesen zu einer oft vergeblichen Bemühung [Instructional explanations during the acquisition of cognitive skills: six theses on an often futile effort]. In I. Hosenfeld & F.-W. Schrader (Eds.), Unterricht und schulische Leistung. Grundlagen, Konsequenzen, Perspektiven (pp. 205–223). Münster: Waxmann.Google Scholar
  92. Romero, M., & Lambropoulos, N. (2011). Internal and external regulation to support knowledge construction and convergence in computer supported collaborative learning (CSCL). Electronic Journal of Research in Educational Psychology, 9(1), 309–330.Google Scholar
  93. Roschelle, J. (1992). Learning by collaborating: convergent conceptual change. Journal of the Learning Sciences, 2, 235–276.Google Scholar
  94. Roschelle, J., Rafanan, K., Estrella, G., Nussbaum, M., & Claro, S. (2009). From handheld collaboration tool to effective classroom module: embedding CSCL in a broader design framework. In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer supported collaborative learning practicesCSCL2009 Conference Proceedings (pp. 395–403). International Society of the Learning Sciences.Google Scholar
  95. Rotering-Steinberg, S. (1992). Gruppenpuzzle und Gruppenrallye. Beispiele für Kooperative Arbeitsformen. [Jigsaw and group rally. Examples of collaborative forms of learning]. Pädagogik, 44(1), 27–30.Google Scholar
  96. Rummel, N., & Spada, H. (2005). Learning to collaborate: an instructional approach to promoting collaborative problem solving in computer-mediated settings. Journal of the Learning Sciences, 14(2), 201–241.Google Scholar
  97. 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: creating a learning world. Proceedings of the Eighth International Conference of the Learning Sciences (ICLS 2008), vol. 3. (pp. 338–345). International Society of the Learning Sciences.Google Scholar
  98. Ruys, I., Van Keer, H., & Aelterman, A. (2011). Student teachers’ skills in the implementation of collaborative learning: a multilevel approach. Teaching and Teacher Education, 27, 1090–1100.Google Scholar
  99. Saab, N., van Joolingen, W. R., & van Hout-Wolters, B. H. A. (2007). Supporting communication in a collaborative discovery learning environment: the effect of instruction. Instructional Science, 35, 73–98.Google Scholar
  100. Salden, R. J. C. M., Koedinger, K. R., Renkl, A., Aleven, V., & McLaren, B. M. (2010). Accounting for beneficial effects of worked examples in tutored problem solving. Educational Psychology Review, 22(4), 379–392.Google Scholar
  101. Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. International Journal of Educational Research, 13(1), 89–99.Google Scholar
  102. Santagata, R., Zannoni, C., & Stigler, J. (2007). The role of lesson analysis in pre-service teacher education: an empirical investigation of teacher learning from a virtual video-based field experience. Journal of Mathematics Teacher Education, 10, 123–140.Google Scholar
  103. Scherrer, J., & Stein, M. K. (2013). Effects of a coding intervention on what teachers learn to notice during whole-group discussion. Journal of Mathematics Teacher Education, 16(2), 105–124.Google Scholar
  104. Schön, D. (1983). The reflective practitioner: how professionals think in action. San Francisco: Jossey-Bass.Google Scholar
  105. Schumann, S., & Eberle, F. (2010). Der Einfluss instruktionaler Unterstützung durch die Lehrperson auf die Entwicklung der Lernmotivation im problemorientierten und im traditionellen Unterricht [The influence of teacher’s instructional support on the development of learning motivation in problem-based and traditional instruction]. Unterrichtswissenschaft, Zeitschrift für Lernforschung, 38(2), 134–151.Google Scholar
  106. Schwarz, B. B., Asterhan, C. S. C., & Gil, J. (2009). Human guidance of synchronous e-discussions: The effects of different moderation scripts on peer argumentation. In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer supported collaborative learning practicesCSCL2009 Conference Proceedings (pp. 497–506). International Society of the Learning Sciences.Google Scholar
  107. Seidel, T., & Prenzel, M. (2008). Wie Lehrpersonen Unterricht wahrnehmen und einschätzen - Erfassung pädagogisch-psychologischer Kompetenzen mit Videosequenzen [How teachers perceive and assess instruction—measurement of pedagogical-psychological competencies with video sequences]. In M. Prenzel, I. Gogolin, & H.-H. Krüger (Eds.), Kompetenzdiagnostik (pp. 201–216). Wiesbaden: VS Verlag für Sozialwissenschaften.Google Scholar
  108. Seidel, T., & Shavelson, R. J. (2007). Teaching effectiveness research in the past decade: the role of theory and research design in disentangling meta-analysis results. Review of Educational Research, 77(4), 454–499.Google Scholar
  109. Sherin, M. G. (2001). Developing a professional vision of classroom events. In T. Wood, B. S. Nelson, & J. Warfield (Eds.), Beyond classical pedagogy: teaching elementary school mathematics (pp. 75–93). Hillsdale: Erlbaum.Google Scholar
  110. Sherin, M. G., & van Es, E. A. (2005). Using video to support teachers’ ability to notice classroom interactions. Journal of Technology and Teacher Education, 13(3), 475–491.Google Scholar
  111. Sherin, M. G., Russ, R., Sherin, B. L., & Colestock, A. (2008). Professional vision in action: an exploratory study. Issues in Teacher Education, 17(2), 27–46.Google Scholar
  112. Slavin, R. E. (1980). Cooperative learning. Review of Educational Research, 50(2), 315–342. doi: 10.3102/00346543050002315.Google Scholar
  113. Slavin, R. E. (1990). Cooperative learning: theory, research, and practice. Boston: Allyn & Bacon.Google Scholar
  114. Slavin, R. E. (1995). Cooperative learning. Theory, research and practice (2nd ed.). Boston: Allyn & Bacon.Google Scholar
  115. Slavin, R. E. (1996). Research on cooperative learning and achievement: what we know, what we need to know. Contemporary Educational Psychology, 21(1), 43–69.Google Scholar
  116. Sluijsmans, D. M. A., Brand-Gruwel, S., & Van Merrienboer, J. J. G. (2002). Peer assessment training in teacher education: effects on performance and perceptions. Assessment & Evaluation in Higher Education, 27(5), 443–545.Google Scholar
  117. Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (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
  118. Sparks-Langer, G. M., Simmons, J. M., Pasch, M., Colton, A., & Starko, A. (1990). Reflective pedagogical thinking: how can we promote it and measure it? Journal of Teacher Education, 41(5), 23–32.Google Scholar
  119. 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.Google Scholar
  120. Stegmann, K., Weinberger, A., & Fischer, F. (2007). Facilitating argumentative knowledge construction with computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(4), 421–447.Google Scholar
  121. Steiner, I. (1972). Group process and productivity. San Diego: Academic.Google Scholar
  122. Strijbos, J.-W. (2011). Assessment of (computer-supported) collaborative learning. IEEE Transactions on Learning Technologies, 4(1), 59–73.Google Scholar
  123. Van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher-student interaction: a decade of research. Educational Psychology Review, 22, 271–296.Google Scholar
  124. Van de Pol, J., Volman, M., Oort, F., & Beishuizen, J. (2013). Teacher scaffolding in small-group work: an intervention study. Journal of the Learning Sciences, 1–51. doi: 10.1080/10508406.2013.805300.
  125. Van Es, E. A., & Sherin, M. G. (2002). Learning to notice: scaffolding new teachers’ interpretations of classroom interactions. Journal of Technology and Teacher Education, 10(4), 571–596.Google Scholar
  126. Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2013). Multidimensional teacher behavior in CSCL. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings, volume 1—full papers & symposia (pp. 518–525). International Society of the Learning Sciences.Google Scholar
  127. Voyiatzaki, E., & Avouris, N. (2009). Alternative ways of monitoring collaborative learning. In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer supported collaborative learning practicesCSCL2009 Conference Proceedings (pp. 180–182). International Society of the Learning Sciences.Google Scholar
  128. Vuorinen, R., Tarkka, M., & Meretoja, R. (2000). Peer evaluation in nurses’ professional development: a pilot study to investigate the issues. Journal of Clinical Nursing, 9, 273–281.Google Scholar
  129. Walker, E., Rummel, N., & Koedinger, K. R. (2009). CTRL: a research framework for providing adaptive collaborative learning support. User Modeling and User-Adapted Interaction, 19(5), 387–431.Google Scholar
  130. Walker, E., Rummel, N., & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning, 6(2), 279–306.Google Scholar
  131. Webb, N. M. (1989). Peer interaction and learning in small groups. International Journal of Educational Research, 13(1), 21–39. doi: 10.1016/0883-0355(89)90014-1.Google Scholar
  132. Webb, N. M. (1991). Task-related verbal interaction and mathematics learning in small groups. Journal for Research in Mathematics Education, 22(5), 366–389.Google Scholar
  133. Webb, N. M., Troper, J. D., & Fall, R. (1995). Constructive activity and learning in collaborative small groups. Journal of Educational Psychology, 87(3), 406–423. doi: 10.1037/0022-0663.87.3.406.Google Scholar
  134. Weinberger, A., Ertl, B., & Fischer, F. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science: An International Journal of Learning and Cognition, 33(1), 1–30.Google Scholar
  135. Westermann, K., & Rummel, N. (2012). Delaying instruction: evidence from a study in a university relearning setting. Instructional Science, 40(4), 673–689.Google Scholar
  136. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: making science accessible to all students. Cognition and Instruction, 16(1), 3–118.Google Scholar
  137. Wichmann, A., & Rummel, N. (2013). Supporting feedback uptake in online peer assessment. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: learning across levels of space, time, and scale: CSCL 2013 Conference Proceedings, volume 2short papers, panels, posters, demos, & community events (pp. 253–254). International Society of the Learning Sciences.Google Scholar
  138. Wiedmann, M., Leach, R. C., Rummel, N., & Wiley, J. (2012). Does group composition affect learning by invention? Instructional Science, 40(4), 711–730. doi: 10.1007/s11251-012-9204-y.Google Scholar
  139. Wiedow, A., & Konradt, U. (2011). Two-dimensional structure of team process improvement: team reflection and team adaptation. Small Group Research, 42(1), 32–54.Google Scholar
  140. Wiener, H. S. (1986). Collaborative learning in the classroom: a guide to evaluation. College English, 48(1), 52–61.Google Scholar
  141. Wilczenski, F. L., Bontrager, T., Ventrone, P., & Correia, M. (2001). Observing collaborative problem-solving processes and outcomes. Psychology in the Schools, 38(3), 269–281.Google Scholar
  142. Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17, 89–100.Google Scholar
  143. Xiaodong, L., Schwartz, D., & Hatano, G. (2005). Toward teachers’ adaptive metacognition. Educational Psychologist, 40, 245–255.Google Scholar
  144. Zeichner, K. M., & Liston, D. P. (1987). Teaching student teachers to reflect. Harvard Educational Review, 56(1), 23–48.Google Scholar
  145. Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory Into Practice, 41, 64–72.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Celia Kaendler
    • 1
    Email author
  • Michael Wiedmann
    • 1
  • Nikol Rummel
    • 2
  • Hans Spada
    • 1
  1. 1.Institute of PsychologyAlbert-Ludwigs-University of FreiburgFreiburgGermany
  2. 2.Institute of Educational ResearchRuhr-Universität BochumBochumGermany

Personalised recommendations