Collaboration Scripts for Enhancing Metacognitive Self-regulation and Mathematics Literacy

  • Cheng-Huan Chen
  • Chiung-Hui ChiuEmail author


This study designed a set of computerized collaboration scripts for multi-touch supported collaborative design-based learning and evaluated its effects on multiple aspects of metacognitive self-regulation in terms of planning and controlling and mathematical literacy achievement at higher and lower levels. The computerized scripts provided a sequence of guidance for structuring intragroup and intergroup interactions and prompting individual metacognitive processes throughout the collaborative design phases based on the Think-Pair-Share method. Four intact classes of 80 fifth-grade students participated in this study. Employing a nonequivalent comparison group quasi-experimental design, this study examined whether or not applying the scripts better enhanced self-regulation and achievement in a technology-infused mathematics learning classroom. Multivariate analyses were conducted to reveal the effects on the aspects among the two sets of variables. The results showed medium effects on the controlling of metacognitive self-regulation and higher level achievement, whereas no significant effects were found for the planning aspect and lower level achievement between the groups with and without the collaboration scripts. The implications of this work in relation to metacognitive processes and technology-infused mathematics learning are discussed based on the results.


Collaboration script Design-based learning Mathematics literacy Metacognitive self-regulation Technology-infused learning environment 



The authors thank the Editor and anonymous reviewers for their remarkably constructive comments. This research was supported by the Ministry of Science and Technology, Taiwan (R.O.C.) under Grant No. NSC 101-2511-S-003-033-MY3.


  1. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R. & Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives, abridged edition (1st ed.). White Plains, NY: Longman.Google Scholar
  2. Apedoe, X. S., Reynolds, B., Ellefson, M. R. & Schunn, C. D. (2008). Bringing engineering design into high school science classrooms: The heating/cooling unit. Journal of Science Education and Technology, 17(5), 454–465. doi: 10.1007/s10956-008-9114-6.CrossRefGoogle Scholar
  3. Basheri, M., Munro, M., Burd, L., & Baghaei, N. (2013). Collaborative learning skills in multi-touch tables for UML software design. International Journal of Advanced Computer Science and Applications, 4(3), 60–66. doi: 10.14569/IJACSA.2013.040311.Google Scholar
  4. Berg, K. F. (1994). Scripted cooperation in high school mathematics: Peer interaction and achievement. Paper presented at the 1994 AERA Annual Meeting, New Orleans, LA.Google Scholar
  5. Bernacki, M. L., Aguilar, A. C. & Byrnes, J. P. (2011). Self-regulated learning and technology enhanced learning environments: An opportunity-propensity analysis. In G. Dettori & D. Persico (Eds.), Fostering self-regulated learning through ICT (pp. 1–26). Hershey: IGI Global.Google Scholar
  6. Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M. & Palincsar, A. (1991). Motivating project-based learning: sustaining the doing, supporting the learning. Educational Psychologist, 26(3–4), 369–398. doi: 10.1080/00461520.1991.9653139.CrossRefGoogle Scholar
  7. Cates, W. M. (1985). A practical guide to educational research (1st ed.). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  8. Chiu, C.-H., Chen, C.-H. & Wu, S.-T. (2013). A multi-touch system for designing tessellations. In T. Bastiaens & G. Marks (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2013 (pp. 2266–2270). Chesapeake, VA: Association for the Advancement of Computing in Education.Google Scholar
  9. Clark, D. R. (2015). Bloom’s taxonomy of learning domains. Retrieved from
  10. Clayphan, A., Kay, J. & Weinberger, A. (2013). ScriptStorm: scripting to enhance tabletop brainstorming. Personal and Ubiquitous Computing, 18(6), 1433–1453. doi: 10.1007/s00779-013-0746-z.CrossRefGoogle Scholar
  11. Collazos, C. A., Guerrero, L. A., Pino, J. A. & Ochoa, S. F. (2002). Evaluating collaborative learning processes. In J. M. Haake & J. A. Pino (Eds.), Groupware: Design, implementation, and use (pp. 203–221). Berlin, Germany: Springer Berlin Heidelberg.CrossRefGoogle Scholar
  12. Dillenbourg, P. (2002). Overscripting 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, The Netherlands: Open Universiteit Nederland.Google Scholar
  13. Dillenbourg, P. & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3(1), 5–23. doi: 10.1007/s11412-007-9033-1.CrossRefGoogle Scholar
  14. Dillenbourg, P. & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, I. Kollar, H. Mandl & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 275–301). New York, NY: Springer US.CrossRefGoogle Scholar
  15. Doppelt, Y., Mehalik, M. M., Schunn, C. D., Silk, E. & Krysinski, D. (2008). Engagement and achievements: a case study of design-based learning in a science context. Journal of Technology Education, 19(2), 22–39.Google Scholar
  16. Fessakis, G., Tatsis, K. & Dimitracopoulou, A. (2008). Supporting “learning by design” activities using group blogs. Educational Technology & Society, 11(4), 199–212.Google Scholar
  17. Fischer, F., Kollar, I., Stegmann, K. & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66. doi: 10.1080/00461520.2012.748005.CrossRefGoogle Scholar
  18. Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–236). Hillsdale, MI: Erlbaum.Google Scholar
  19. Fortus, D., Dershimer, R. C., Krajcik, J., Marx, R. W. & Mamlok-Naaman, R. (2004). Design-based science and student learning. Journal of Research in Science Teaching, 41(10), 1081–1110. doi: 10.1002/tea.20040.CrossRefGoogle Scholar
  20. Gardner, G. E. (2012). Using biomimicry to engage students in a design-based learning activity. The American Biology Teacher, 74(3), 182–184. doi: 10.1525/abt.2012.74.3.10.CrossRefGoogle Scholar
  21. Ge, X. & Land, S. M. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology Research and Development, 51(1), 21–38. doi: 10.1007/BF02504515.CrossRefGoogle Scholar
  22. Gómez Puente, S. M., van Eijck, M. & Jochems, W. (2013). Facilitating the learning process in design-based learning practices: An investigation of teachers’ actions in supervising students. Research in Science & Technological Education, 31(3), 288–307. doi: 10.1080/02635143.2013.837043.CrossRefGoogle Scholar
  23. Hadwin, A. F. & Winne, P. H. (2001). CoNoteS2: A software tool for promoting self-regulation. Educational Research and Evaluation, 7(2–3), 313–334. doi: 10.1076/edre.7.2.313.3868.CrossRefGoogle Scholar
  24. Han, S., & Bhattacharya, K. (2001). Constructionism, learning by design, and project based learning. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Bloomington, IN: Association for Educational Communications and Technology. Retrieved from
  25. Harris, A., Rick, J., Bonnett, V., Yuill, N., Fleck, R., Marshall, P. & Rogers, Y. (2009). Around the table: Are multiple-touch surfaces better than single-touch for children’s collaborative interactions? In C. O’Malley et al. (Eds.), Proceedings of the 9th International Conference on Computer supported Collaborative Learning (Vol. 1, pp. 335–344). Pittsburgh, PA: International Society of the Learning Sciences.Google Scholar
  26. Hsu, Y.-S., Yen, M.-H., Chang, W.-H., Wang, C.-Y. & Chen, S. (2014). Content analysis of 1998–2012 empirical studies in science reading using a self-regulated learning lens. International Journal of Science and Mathematics Education. doi: 10.1007/s10763-014-9574-5, Advance online publication.Google Scholar
  27. Iiskala, T., Vauras, M., Lehtinen, E. & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379–393. doi: 10.1016/j.learninstruc.2010.05.002.CrossRefGoogle Scholar
  28. Jang, S.-J. (2010). The impact on incorporating collaborative concept mapping with coteaching techniques in elementary science classes. School Science and Mathematics, 110(2), 86–97. doi: 10.1111/j.1949-8594.2009.00012.x.CrossRefGoogle Scholar
  29. Ke, F. (2014). An implementation of design-based learning through creating educational computer games: a case study on mathematics learning during design and computing. Computers & Education, 73, 26–39. doi: 10.1016/j.compedu.2013.12.010.CrossRefGoogle Scholar
  30. Kharrufa, A., Leat, D. & Olivier, P. (2010). Digital mysteries: Designing for learning at the tabletop. In proceedings of the 5th ACM international conference on interactive tabletops and surfaces (pp. 197–206). New York: ACM.Google Scholar
  31. 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 (Vol. 6, pp. 13–37). New York, NY: Springer.CrossRefGoogle Scholar
  32. Kollar, I., Fischer, F. & Hesse, F. W. (2006). Collaboration scripts—a conceptual analysis. Educational Psychology Review, 18(2), 159–185. doi: 10.1007/s10648-006-9007-2.CrossRefGoogle Scholar
  33. Kollar, I., Ufer, S., Reichersdorfer, E., Vogel, F., Fischer, F. & Reiss, K. (2014). Effects of collaboration scripts and heuristic worked examples on the acquisition of mathematical argumentation skills of teacher students with different levels of prior achievement. Learning and Instruction, 32, 22–36. doi: 10.1016/j.learninstruc.2014.01.003.CrossRefGoogle Scholar
  34. Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J. & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: putting learning by design™ into practice. The Journal of the Learning Sciences, 12(4), 495–547. doi: 10.1207/S15327809JLS1204_2.CrossRefGoogle Scholar
  35. Kramarski, B., Mevarech, Z. R. & Arami, M. (2002). The effects of metacognitive instruction on solving mathematical authentic tasks. Educational Studies in Mathematics, 49(2), 225–250. doi: 10.1023/A:1016282811724.CrossRefGoogle Scholar
  36. Kramarski, B. & Mizrachi, N. (2006). Online discussion and self-regulated learning: Effects of instructional methods on mathematical literacy. The Journal of Educational Research, 99(4), 218–231. doi: 10.3200/JOER.99.4.218-231.CrossRefGoogle Scholar
  37. Krishnamurthi, M. (2012). Instructional guide for university faculty and teaching assistants. DeKalb, IL: Faculty Development and Instructional Design Center, Northern Illinois University.Google Scholar
  38. Lameijer, E.-W. (2011). Metacognition: The third way of thinking. Retrieved from
  39. Lee, Y.-H. & Wu, J.-Y. (2013). The indirect effects of online social entertainment and information seeking activities on reading literacy. Computers & Education, 67, 168–177. doi: 10.1016/j.compedu.2013.03.001.CrossRefGoogle Scholar
  40. Lyman, F. T. (1981). The responsive classroom discussion: The inclusion of all students. In A. S. Anderson (Ed.), Mainstreaming digest: A collection of faculty and student papers (pp. 109–113). College Park, MD: College of Education, University of Maryland.Google Scholar
  41. Meyer, D. K., Turner, J. C. & Spencer, C. A. (1997). Challenge in a mathematics classroom: Students’ motivation and strategies in project-based learning. The Elementary School Journal, 97(5), 501–521.CrossRefGoogle Scholar
  42. 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: Lawrence Erlbaum Associates.Google Scholar
  43. O’Donnell, A. M. & Dansereau, D. F. (1992). Scripted cooperation in student dyads: A method for analysing 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
  44. Papert, S. (1990). Introduction. In I. Harel (Ed.), Constructionist learning (pp. 1–8). Boston, MA: MIT Media Laboratory.Google Scholar
  45. Paquette, G. (2004). Educational modeling languages, from an instructional engineering perspective. In R. McGreal (Ed.), Online education using learning objects (pp. 331–346). Abingdon, England: RoutledgeFalmer.Google Scholar
  46. Pintrich, P. R. & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. doi: 10.1037/0022-0663.82.1.33.CrossRefGoogle Scholar
  47. Pintrich, P. R., Smith, D. A. F., Garcia, T. & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: National Center for Research to Improve Postsecondary Teaching and Learning.Google Scholar
  48. Puntambekar, S. & Kolodner, J. L. (2005). Toward implementing distributed scaffolding: helping students learn science from design. Journal of Research in Science Teaching, 42(2), 185–217. doi: 10.1002/tea.20048.CrossRefGoogle Scholar
  49. Rogers, M. A. P., Cross, D. I., Gresalfi, M. S., Trauth-Nare, A. E. & Buck, G. A. (2011). First year implementation of a project-based learning approach: the need for addressing teachers’ orientations in the era of reform. International Journal of Science and Mathematics Education, 9(4), 893–917. doi: 10.1007/s10763-010-9248-x.CrossRefGoogle Scholar
  50. Schneider, W. & Artelt, C. (2010). Metacognition and mathematics education. ZDM, 42(2), 149–161. doi: 10.1007/s11858-010-0240-2.CrossRefGoogle Scholar
  51. Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In D. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). New York, NY: Macmillan Publishing.Google Scholar
  52. Shin, N., & McGee, S. (2003). Designers should enhance students’ ill-structured problem-solving skills. Retrieved from
  53. Silk, E. M., Higashi, R., Shoop, R. & Schunn, C. D. (2010). Designing technology activities that teach mathematics. The Technology Teacher, 69(4), 21–27.Google Scholar
  54. 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
  55. Stoffa, R., Kush, J. C., & Heo, M. (2011). Using the motivated strategies for learning questionnaire and the strategy inventory for language learning in assessing motivation and learning strategies of Generation 1.5 Korean immigrant students. Education Research International, 2011, article ID 491276. doi: 10.1155/2011/491276.
  56. The Organisation for Economic Co-operation and Development (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy (1st ed.). Paris, France: Author.Google Scholar
  57. U.S. Department of Education (2014). STEM literacy. Retrieved from
  58. Weinberger, A., Ertl, B., Fischer, F. & Mandl, H. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33(1), 1–30. doi: 10.1007/s11251-004-2322-4.CrossRefGoogle Scholar
  59. Wu, J.-J. & Cherng, B.-L. (1992). Motivated Strategies for Learning Questionnaire (MSLQ): A revised version for use with Chinese elementary and junior high school students. Psychological Testing, 39, 59–78 [In Chinese].Google Scholar
  60. Yang, K.-Y. & Heh, J.-S. (2007). The impact of internet virtual physics laboratory instruction on the achievement in physics, science process skills and computer attitudes of 10th-grade students. Journal of Science Education and Technology, 16(5), 451–461. doi: 10.1007/s10956-007-9062-6.CrossRefGoogle Scholar

Copyright information

© Ministry of Science and Technology, Taiwan 2015

Authors and Affiliations

  1. 1.Graduate Institute of Information and Computer EducationNational Taiwan Normal UniversityTaipeiRepublic of China

Personalised recommendations