Advertisement

Scripted collaborative learning with the cognitive tutor algebra

  • Nikol Rummel
  • Dejana Mullins
  • Hans Spada
Article

Abstract

With the aim to promote students’ mathematics learning, we extended the Cognitive Tutor Algebra (CTA), a computer-based tutoring system for high school mathematics, to a collaborative setting. Furthermore we developed a collaboration script to support students’ interactions. In an experimental classroom study, we compared three conditions: scripted collaborative learning, unscripted collaborative learning, and individual learning. After a 2-day learning phase, posttests assessed individual and collaborative reproduction of knowledge and skills, and future learning. First, with the collaboration script we aimed to improve students’ interaction. Second, we assumed that due to an improved interaction students would benefit more from the learning opportunities during collaboration and, in consequence, their learning would increase as compared with the other conditions. To investigate the first assumption, we compared the interaction of a scripted dyad and an unscripted dyad. The in-depth process analyses revealed a positive impact of the script on student collaboration and problem solving during scripted interaction and in subsequent unscripted interaction. While this effect was mirrored in the learning gains of the two dyads, we could not establish a general learning effect in the quantitative between-condition comparison of student performance. Particularly for students with low prior knowledge, the removal of the script in the test phase initially entailed a decline in reproduction performance as students had to get used to the unscripted problem-solving situation. A notable finding was, however, that the collaborative conditions yielded the same outcomes as the individual condition in the individual reproduction test even though students had solved fewer problems during the learning phase and had only solved them collaboratively.

Keywords

Computer-supported collaborative learning Scripted collaboration Mathematics learning Experimental classroom study Collaboration process analysis 

Notes

Acknowledgements

This research was supported by the Pittsburgh Science of Learning Center, NSF Grant # 0354420, by the Baden-Württemberg Stiftung, Germany, and by the Virtual PhD Programm, VGK (DFG). We thank Bruce McLaren for his valuable contributions during initial stages of the project. We are grateful to Jonathan Steinhart, Erin Walker, Dale Walters and Sung-Joo Lim for their support concerning the technical implementations of the Tutor environment; and to Kathy Dickensheets and Lars Holzäpfel for their support in “getting the math right”. Further we would like to express our gratitude to the teachers from CWCTC for their motivated involvement in the project. Also, we would like to thank our student research assistants Martina Rau and Katharina Westermann, and Michael Wiedmann, for their help on data coding and data analysis. Special thanks go to Katharina Westermann for her help in preparing this manuscript.

References

  1. Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modelling to meta-cognitive skills. In J. C. Lester, R. M. Vicari, & F. Paraguaçu (Eds.), Proceedings of Seventh International Conference on Intelligent Tutoring Systems, ITS 2004 (pp. 227–239). Berlin: Springer.Google Scholar
  2. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  3. Aronson, E., Blaney, N., Sikes, J., Stephan, C., & Snapp, M. (1978). The jigsaw classroom. Beverly Hills: Sage.Google Scholar
  4. Artelt, C. (2000). Strategisches Lernen [Strategic learning]. Münster: Waxmann.Google Scholar
  5. Avouris, N., Fiotakis, G., Kahrimanis, G., Margaritis, M., & Komis, V. (2007). Beyond logging of fingertip actions: Analysis of collaborative learning using multiple sources of data. Journal of Interactive Learning Research, 18(2), 231–250.Google Scholar
  6. Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2004). Detecting student misuse of intelligent tutoring systems. Paper presented at the Proceedings of the 7th International Conference on Intelligent Tutoring Systems.Google Scholar
  7. Berg, K. F. (1993). Structured cooperative learning and achievement in a high school mathematics class. Paper presented at the Annual Meeting of the American Educational Research Association, Atlanta.Google Scholar
  8. Berg, K. F. (1994). Scripted cooperation in high school mathematics: Peer interaction and achievement. Paper presented at the Annual meeting of the American Educational Research Association, New Orleans, Louisana.Google Scholar
  9. Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Educational Research, 24, 61–100.Google Scholar
  10. Cress, U. (2008). The need for considering multi-level analysis in CSCL research. An appeal for the use of more advanced statistical methods. International Journal of Computer-Supported Collaborative Learning, 3(1), 69–84.CrossRefGoogle Scholar
  11. 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: Open Univeriteit Nederland.Google Scholar
  12. Dillenbourg, P., & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, I. Kollar, H. Mandl, & J. Haake (Eds.), Scripting computer-supported collaborative learning. Cognitive, computational, and educational perspectives (pp. 275–301). New York: Springer.CrossRefGoogle Scholar
  13. 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 (pp. 189–211). Oxford: Elsevier/Pergamon.Google Scholar
  14. Diziol, D., Rummel, N., Spada, H., & McLaren, B. (2007). Promoting learning in mathematics: Script support for collaborative problem solving with the Cognitive Tutor Algebra. In C. A. Chinn, G. Erkens & S. Puntambekar (Eds.), Mice, minds and society. Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference 2007, Vol 8, I (pp. 39–41). International Society of the Learning Sciences, Inc. ISSN 1819-0146Google Scholar
  15. Diziol, D., Walker, E., Rummel, N., & Koedinger, K. (2010). Using intelligent tutor technology to implement adaptive support for student collaboration. Educational Psychology Review, 22(1), 89–102.CrossRefGoogle Scholar
  16. Dubinsky, E., Mathews, D., & Reynolds, B. E. (Eds.). (1997). Readings in cooperative learning for undergraduate mathematics. Washington: Mathematical Association of America.Google Scholar
  17. Field, A. P. (2005). Discovering statistics using SPSS (2nd ed.). London: Sage.Google Scholar
  18. Hausmann, R. G. M., Chi, M. T. H., & Roy, M. (2004). Learning from collaborative problem solving: An analysis of three hypothesized mechanisms. In K. D. Forbus, D. Gentner, & T. Regier (Eds.), 26nd Annual Conference of the Cognitive Science Society (pp. 547–552). Mahwah: Erlbaum.Google Scholar
  19. Huitema, B. E. (1980). The analysis of covariance and alternatives. New York: Wiley.Google Scholar
  20. Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (Vol. 1) (4th ed., pp. 233–265). Boston: McGraw-Hill.Google Scholar
  21. King, A. (2007). Scripting collaborative learning processes: A cognitive perspective. In F. Fischer, I. Kollar, H. Mandl, & J. Haake (Eds.), Scripting computer-supported collaborative learning. Cognitive, computational, and educational perspectives (pp. 18–19). New York: Springer.Google Scholar
  22. Koedinger, K. R. (1998, June 5–6, 1998). Intelligent cognitive tutors as modeling tool and instructional model. Paper presented at the NCTM Standards 2000 Technology Conference.Google Scholar
  23. 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
  24. Koedinger, K. R., Corbett, A. T., Ritter, S., & Shapiro, L. J. (2000). Carnegie Learning’s Cognitive Tutor ™: Summary Research Results. Retrieved January 16, 2006, from http://www.carnegielearning.com/approach_research_reports.cfm
  25. Kollar, I., Fischer, F., & Hesse, F. W. (2006). Computer-supported collaboration scripts—a conceptual analysis. Educational Review, 18(2), 159–185.Google Scholar
  26. Kollar, I., Fischer, F., & Slotta, J. D. (2007). Internal and external scripts in computer-supported collaborative inquiry learning. Learning & Instruction, 17(6), 708–721.CrossRefGoogle Scholar
  27. Levin, J. R. (2004). Random thoughts on the (in)credibility of educational-psychological intervention research. Educational Psychologist, 39(3), 173–184.CrossRefGoogle Scholar
  28. Lou, Y., Abrami, P. C., & d’Apollonia, S. (2001). Small group and individual learning with technology: A meta-analysis. Review of Educational Research, 71(3), 449–521.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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. doi: 10.1007/s11412-011-9122-z.CrossRefGoogle Scholar
  31. National Council of Teachers of Mathematics. (2006). Overview of principles and standards for school mathematics. Retrieved June 5, 2006, from http://www.nctm.org/standards/overview.htm
  32. 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). Erlbaum.Google Scholar
  33. Organisation for Economic Co-operation and Development [OECD] (n.d.). Programme for International Student Assessment. Retrieved May 25, 2005, from http://www.pisa.oecd.org/pages/0,2966,en_32252351_32235968_1_1_1_1_1,00.html
  34. Reimann, P. (2007). Time is precious: Why process analysis is essential for CSCL (and can also help to bridge between experimental and descriptive methods. In C. A. Chinn, G. Erkens & S. Puntambekar (Eds.), Mice, minds and society. Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference 2007, Vol 8, II (pp. 590–607). International Society of the Learning Sciences.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. Rummel, N., & Spada, H. (2007). Can people learn computer-mediated collaboration by following a script? In F. Fischer, H. Mandl, J. M. Haake, & I. Kollar (Eds.), Scripting computer-supported communication of knowledge Cognitive, computational, and educational perspectives (pp. 39–55). New York: Springer.Google Scholar
  37. Rummel, N., Deiglmayr, A., Spada, H., Karimanis, G., & Avouris, N. (2011). Analyzing collaborative interactions across domains and settings: An adaptable rating scheme. In S. Puntambekar, C. Hmelo-Silver, & G. Erkens (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 367–390). Berlin: Springer.CrossRefGoogle Scholar
  38. 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: Cambridge University Press.Google Scholar
  39. 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.CrossRefGoogle Scholar
  40. Teasley, S. D. (1995). The role of talk in children’s peer collaborations. Developmental Psychology, 31(2), 207–220.CrossRefGoogle Scholar
  41. Walker, E., Rummel, N., & Koedinger, K. R. (2008). To tutor the tutor: Adaptive domain support for peer tutoring. In B. P. Woolf, E. Aïmeur, R. Nkambou, & S. P. Lajoie (Eds.), Proceedings of the Ninth International Conference on Intelligent Tutoring Systems (ITS 2008), Lecture Notes in Computer Science, Vol. 5091 (pp. 626–635). Springer, ISBN 978-3-540-69130-3Google Scholar
  42. Walker, E., Rummel, N., & Koedinger, K. (2009a). CTRL: A research framework for providing adaptive collaborative learning support. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), 19(5), 387–431.CrossRefGoogle Scholar
  43. Walker, E., Rummel, N., & Koedinger, K. (2009b). Integrating collaboration and intelligent tutoring data in evaluation of a reciprocal peer tutoring environment. Research and Practice in Technology Enhanced Learning, 4(3), 221–251.CrossRefGoogle Scholar
  44. Walker, E., Rummel, N., & Koedinger, K. (2010). Automated adaptive support for peer tutoring in high-school mathematics. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the Disciplines. Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010), Vol 2 (pp. 151–153). International Society of the Learning Sciences, Inc.Google Scholar
  45. Walker, E., Rummel, N., & Koedinger, K. (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.CrossRefGoogle Scholar
  46. 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.CrossRefGoogle Scholar
  47. Wecker, C., Kollar, I., Fischer, F., & Prechtl, H. (2010). Fostering online search competence and domain-specific knowledge in inquiry classrooms: Effects of continuous and fading collaboration scripts. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the disciplines. Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010), Vol. 1 (pp. 810–817). International Society of the Learning Sciences, Inc.Google Scholar
  48. Westermann, K., & Rummel, N. (2012). Delaying instruction—Evidence from a study in a university relearning setting. Instructional Science. doi: 0.1007/s11251-012-9207-8.

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2012

Authors and Affiliations

  1. 1.Institute of Educational ResearchRuhr-Universität BochumBochumGermany
  2. 2.Institute of PsychologyAlbert-Ludwigs-Universität FreiburgFreiburg im BreisgauGermany

Personalised recommendations