Overcoming Deceptive Clarity by Encouraging Metacognition in the Web-Based Inquiry Science Environment

  • Jennifer L. Chiu
  • Jennifer King Chen
  • Marcia C. Linn
Part of the Springer International Handbooks of Education book series (SIHE, volume 28)


In our research we view metacognition and cognition as interacting processes that together promote coherent understanding. We propose that the use of the knowledge integration pattern to design instructional scaffolding encourages the interplay between these two processes. In this chapter, we present and discuss findings that indicate that instructional activities designed using the knowledge integration pattern promote student learning from dynamic visualizations by helping to overcome deceptive clarity.


Knowledge Integration Metacognitive Skill Scientific Phenomenon Metacognitive Process Dynamic Visualization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This material is based upon work ­supported by the National Science Foundation under grants No. ESI-0334199 and ESI-0455877. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors appreciate helpful comments from the Technology-Enhanced Learning in Science research group.


  1. Ainsworth, S. (2008). How do animations influence learning? In D. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning. Charlotte, NC: Information Age Publishing.Google Scholar
  2. Aleven, V., & Koedinger, K. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147–179.CrossRefGoogle Scholar
  3. Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, 199–209.CrossRefGoogle Scholar
  4. Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30(1), 87–111.CrossRefGoogle Scholar
  5. Azevedo, R., Moos, D., Greene, J., Winters, F., & Cromley, J. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development, 56(1), 46–72.Google Scholar
  6. Azevedo, R., Winters, F. I., & Moos, D. C. (2004). Can students collaboratively use hypermedia to learn about science? The dynamics of self- and other-regulatory processes in an ecology classroom. Journal of Educational Computing Research, 31, 215–245.CrossRefGoogle Scholar
  7. Bjork, R. A., & Linn, M. C. (2006). The science of learning and the learning of science: Introducing desirable difficulties. APS Observer, 19, 29.Google Scholar
  8. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: Brain, mind, experience and school. Washington, DC: National Research Council.Google Scholar
  9. Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178.CrossRefGoogle Scholar
  10. Chang, H.-Y., Quintana, C., & Krajcik, J. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(1), 73–94.Google Scholar
  11. Chi, M. T. H. (2000). Self-explaining: The dual process of generating inference and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–238). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  12. Chi, M. T. H., De Leew, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.Google Scholar
  13. Chiu, J. L. (2010). Supporting students’ knowledge integration with technology-enhanced inquiry curricula (Doctoral dissertation). Dissertation and Theses Database. (UMI No. AAT 3413337).Google Scholar
  14. Chiu, J., & Linn, M. C. (2008). Self-assessment and self-explanation for learning chemistry using dynamic molecular visualizations. In international perspectives in the learning sciences: Cre8ting a learning world. Proceedings of the 8th International Conference of the Learning Sciences (Vol. 3, pp. 16–17). Utrecht, The Netherlands: International Society of the Learning Sciences, Inc.Google Scholar
  15. Cromley, J. G., Azevedo, R., & Olson, E. D. (2005). Self-regulation of learning with multiple representations in hypermedia. In C.-K. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.), Artificial intelligence in education: Supporting learning through intelligent and socially informed technology (pp. 184–191). Amsterdam, The Netherlands: IOS Press.Google Scholar
  16. Davis, E. A., & Linn, M. C. (2000). Scaffolding students’ knowledge integrations: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819–837.CrossRefGoogle Scholar
  17. diSessa, A. (2000). Changing minds: Computers, learning and literacy. Cambridge, MA: MIT Press.Google Scholar
  18. Ertmer, P. A., & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24, 1–24.CrossRefGoogle Scholar
  19. Graesser, A., McNamara, D., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Pint and Query, AuthTutor and iSTRAT. Educational Psychologist, 40(4), 225–234.CrossRefGoogle Scholar
  20. Greene, J., & Azevedo, R. (2007). Adolescents’ use of self-regulatory processes and their relation to qualitative mental model shifts while using hypermedia. Journal of Educational Computing Research, 36(2), 125–148.CrossRefGoogle Scholar
  21. Hegarty, M., Kriz, S., & Cate, C. (2003). The roles of mental animations and external animations in understanding mechanical systems. Cognition and Instruction, 21(4), 325–360.CrossRefGoogle Scholar
  22. Hoffler, T., & Leutner, D. (2007). Instructional animations versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738.CrossRefGoogle Scholar
  23. Kalyuga, S. (2007). Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective. Educational Psychology Review, 19(3), 387–399.CrossRefGoogle Scholar
  24. King Chen, J. Y., Tinker, R., & McElhaney, K. (2011). Supporting student understanding of projectile and orbital motion with dynamic models. Poster presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
  25. Kombartzky, U., Ploetzner, R., Schlag, S., & Metz, B. (2010). Developing and evaluating a strategy for learning from animations. Learning and Instruction, 20, 424–433.CrossRefGoogle Scholar
  26. Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 989–998.Google Scholar
  27. Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction, 13(2), 177–189.CrossRefGoogle Scholar
  28. Linn, M. C., Chang, H.-Y., Chiu, J. L., Zhang, H., & McElhaney, K. (2010). Can desirable difficulties overcome deceptive clarity in scientific visualizations? In A. Benjamin (Ed.), Successful remembering and successful forgetting: A Festschrift in honor of Robert A. Bjork. New York: Routledge.Google Scholar
  29. Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science Education, 87, 517–538.CrossRefGoogle Scholar
  30. Linn, M. C., & Eylon, B.-S. (2006). Science education. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed.). Mahwah, NJ: Erlbaum.Google Scholar
  31. Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York: Routledge.Google Scholar
  32. Linn, M. C., Eylon, B. S., & Davis, E. A. (2004). The knowledge integration perspective on learning. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 73–83). Mahwah, NJ: Erlbaum.Google Scholar
  33. Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10(10), 464–470.CrossRefGoogle Scholar
  34. Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257–274.CrossRefGoogle Scholar
  35. McElhaney, K. W. (2010). Making controlled experimentation more informative in inquiry investigations (Doctoral dissertation). Dissertation and Theses Database. (UMI No. AAT 3413549).Google Scholar
  36. Moos, D. C., & Azevedo, R. (2008). Self-regulated learning with hypermedia: The role of prior knowledge. Contemporary Educational Psychology, 33, 270–298.Google Scholar
  37. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326.CrossRefGoogle Scholar
  38. Najafi, H., & Slotta, J. (2010). Analyzing equality of participation in collaborative inquiry: Toward a knowledge community. Proceedings of the 9th International Conference of the Learning Sciences, Chicago, IL.Google Scholar
  39. Plass, J. L., Homer, B. D., & Hayward, E. (2009). Design factors for educationally effective animations and simulations. Journal of Computing in Higher Education, 21(1), 31–61.CrossRefGoogle Scholar
  40. Quintana, C., Zhang, M., & Krajcik, J. (2005). A framework for supporting metacognitive aspects of online inquiry through software-based scaffolding. Educational Psychologist, 40(4), 235–2244.CrossRefGoogle Scholar
  41. Reiber, L. P., Tzeng, S., & Tribble, K. (2004). Discovery learning, representation, and explanation within a computer-based simulation: Finding the right mix. Learning and Instruction, 14, 307–323.Google Scholar
  42. Renkl, A., & Atkinson, R. K. (2002). Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive Learning Environments, 10, 105–119.CrossRefGoogle Scholar
  43. Richland, L. E., Linn, M. C., & Bjork, R. A. (2007). Cognition and instruction: Bridging laboratory and classroom settings. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky, & T. Perfect (Eds.), Handbook of applied cognition (2nd ed.). New York: Wiley.Google Scholar
  44. Rozenblit, L. R., & Keil, F. C. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26, 521–562.CrossRefGoogle Scholar
  45. Schank, P., & Kozma, R. (2002). Learning chemistry through the use of a representation-based knowledge building environment. Journal of Computers in Mathematics and Science Teaching, 2(3), 254–271.Google Scholar
  46. Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research and Development. Special Issue: Research on Cognitive Load Theory and Its Design Implications for E-Learning, 53(3), 47–58.CrossRefGoogle Scholar
  47. Shen, J., & Linn, M. C. (2010). A technology-enhanced unit of modeling static electricity: Integrating scientific explanations and everyday observations. International Journal of Science Education. doi: 10.1080/09500693.2010.514012.
  48. Slotta, J., & Linn, M. C. (2009). WISE Science: Web-based inquiry in the classroom. New York: Teachers College Press.Google Scholar
  49. Slotta, J. & Peters, V. (2008). A blended model for knowledge communities: Embedding scaffolded inquiry. Proceedings of the International Conference of the Learning Sciences. Utrecht, Netherlands.Google Scholar
  50. Songer, N., & Linn, M. C. (2006). How do students’ views of science influence knowledge integration? Journal of Research in Science Teaching, 28(9), 761–784.CrossRefGoogle Scholar
  51. Tate, E. (2009). Asthma in the community: Designing instruction to help students explore scientific dilemmas that impact their lives (Doctoral dissertation). Dissertation and Theses Database. (UMI No. AAT 3383554).Google Scholar
  52. Tinker, R. (2009). In Visualizing to integrate science understanding for all learners (VISUAL), NSF Discovery Research K-12 grant proposal, #0918743.Google Scholar
  53. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human Computer Studies, 57(4), 247–262.CrossRefGoogle Scholar
  54. Vygotsky, 1. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.Google Scholar
  55. White, B., & Frederiksen, J. (1998). Inquiry, modeling and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.CrossRefGoogle Scholar
  56. White, B., & Frederiksen, J. (2000). Has been deleted from the text and substituted with the 2005 reference.Google Scholar
  57. White, B., & Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4), 211–223.CrossRefGoogle Scholar
  58. Wouters, P., Paas, F., & van Merrienboer, J. J. G. (2008). How to optimize learning from animated models: A review of guidelines based on cognitive load. Review of Educational Research, 78(3), 645–675.CrossRefGoogle Scholar
  59. Zhang, Z., & Linn, M. C. (2008). Using drawings to support learning from dynamic visualizations. In International perspectives in the learning sciences: Creating a learning world. Proceedings of the 8th International Conference of the Learning Sciences (Vol. 3, pp. 161–162). Utrecht, The Netherlands: International Society of the Learning Sciences, Inc.Google Scholar
  60. Zimmerman, B. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166–183.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jennifer L. Chiu
    • 1
  • Jennifer King Chen
    • 2
  • Marcia C. Linn
    • 2
  1. 1.Science, Technology, Engineering and Math (STEM) EducationCurry School of Education, University of VirginiaCharlottesvilleUSA
  2. 2.Education in Mathematics, Science, and TechnologyUniversity of CaliforniaBerkeleyUSA

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