Journal of Science Education and Technology

, Volume 22, Issue 5, pp 702–717 | Cite as

How Dynamic Visualization Technology can Support Molecular Reasoning

Article

Abstract

This paper reports the results of a study aimed at exploring the advantages of dynamic visualization for the development of better understanding of molecular processes. We designed a technology-enhanced curriculum module in which high school chemistry students conduct virtual experiments with dynamic molecular visualizations of solid, liquid, and gas. They interact with the visualizations and carry out inquiry activities to make and refine connections between observable phenomena and atomic level processes related to phase change. The explanations proposed by 300 pairs of students in response to pre/post-assessment items have been analyzed using a scale for measuring the level of molecular reasoning. Results indicate that from pretest to posttest, students make progress in their level of molecular reasoning and are better able to connect intermolecular forces and phase change in their explanations. The paper presents the results through the lens of improvement patterns and the metaphor of the “ladder of molecular reasoning,” and discusses how this adds to our understanding of the benefits of interacting with dynamic molecular visualizations.

Keywords

High school chemistry Simulations Technology-enhanced learning in science 

References

  1. Ainsworth S (2008) The educational value of multiple-representations when learning complex scientific concepts. In: Gilbert JK, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education. Springer, Dordrecht, pp 191–208CrossRefGoogle Scholar
  2. Ainsworth S, Van Labeke N (2004) Multiple forms of dynamic representation. Learn Instruction 14:241–255CrossRefGoogle Scholar
  3. Bailey E, Worth B (2004). Making thinking visible: enhancing media literacy instruction. Unpublished design study report, Stanford University School of Education (Prof. Daniel Schwartz)Google Scholar
  4. Berenfeld B, Tinker R (2001) Revolutionizing science teaching with atomic scale models. Paper presented at the national education computer conference (NECC). Chicago, ILGoogle Scholar
  5. Bogdan RC, Biklen SK (1998) Qualitative research for education: an introduction to theory and methods, 3rd edn. Allyn & Bacon, MassGoogle Scholar
  6. Buckley B, Gobert J, Kindfield ACH, Horwitz P, Tinker R, Gerlits B (2004) Model-based teaching and learning with BioLogica: what do they learn? How do they learn? How do we know? J Sci Educ Technol 13(1):23–41CrossRefGoogle Scholar
  7. Clark DB, Sampson V (2008) Assessing dialogic argumentation in online environments to relate structure, grounds, and conceptual quality. J Res Sci Teach 45(3):293–321CrossRefGoogle Scholar
  8. Clark DB, Varma K, McElhaney K, Chiu J (2008) Design rationale within TELS projects to support knowledge integration. In: Robinson D, Schraw G (eds) Recent innovations in educational technology that facilitate student learning. Information Age Publishing, Charlotte, NC, pp 157–193Google Scholar
  9. Crouch C, Fagen AP, Callan JP, Mazur E (2004) Classroom demonstrations: learning tools or entertainment? Am J Phys 72(6):835–857CrossRefGoogle Scholar
  10. Dean S, Illowsky B (2012) Descriptive statistics: box plot. Retrieved September 15, 2012 from the Connexions Web site: http://cnx.org/content/m16296/1.12/
  11. Dori YJ, Hameiri M (2003) Multidimensional analysis system for quantitative chemistry problems—symbol, macro, micro and process aspects. J Res Sci Teach 40(3):278–302CrossRefGoogle Scholar
  12. Feurzeig W, Roberts N (1999) Modeling and simulation in science and mathematics education. Springer, New YorkCrossRefGoogle Scholar
  13. Finkelstein ND, Adams WK, Keller CJ, Kohl PB, Perkins KK, Podolefsky NS (2005) When learning about the real world is better done virtually: a study of substituting computer simulations for laboratory equipment. Phys Rev Special Topics Phys Educat Res 1(1):10103–10110CrossRefGoogle Scholar
  14. Fredette M (2012) Curriculum update: biology evolved. Transform Educat Through Technol 39(6):16–19Google Scholar
  15. Gerjets P, Imhof B, Kühl T, Pfeiffer V, Scheiter K, Gemballa S (2010) Using static and dynamic visualizations to support the comprehension of complex dynamic phenomena in the natural sciences. In: Verschaffel L, de Corte E, de Jong T, Elen J (eds) Use of external representations in reasoning and problem solving: analysis and improvement. Routledge, London, pp 153–168Google Scholar
  16. Goldberg F (2001) Some roles of computer technology in helping students learn physics: computer simulations. Paper presented at international conference on computer and information technology in physics education, Manila, Philippines. Retrieved September 30, 2012 from http://www.sci.sdsu.edu/CRMSE/old_site/FG/pub1.pdf
  17. Guzdial M (2011) Eric Mazur’s Keynote at ICER 2011: observing demos hurts learning, and confusion is a sign of understanding. Computing Education Blog, Aug 17, 2011. Retrieved September 26, 2011 from http://computinged.wordpress.com/2011/08/17/eric-mazurs-keynote-at-icer-2011-observing-demos-hurts-learning-and-confusion-is-a-sign-of-understanding/
  18. Hegarty M (2004) Dynamic visualizations and learning: getting to the difficult questions. Learn Instr 14:343–351CrossRefGoogle Scholar
  19. Kali Y, Linn MC (2008) Designing effective visualization for elementary school science. Elementary School J 109(2):181–198CrossRefGoogle Scholar
  20. Kapitanoff SH (2009) Collaborative testing: cognitive and interpersonal processes related to enhanced test performance. Active Learn Higher Educat 10:56–70CrossRefGoogle Scholar
  21. Kozma R (2003) Material and social affordances of multiple representations for science understanding. Learn Instr 13(2):205–226CrossRefGoogle Scholar
  22. Lee H-S, Linn MC, Varma K, Liu OL (2010) How do technology-enhanced inquiry science units impact classroom learning? J Res Sci Teach 47(1):71–90CrossRefGoogle Scholar
  23. Levy D (2002) Collaborative conceptual change: the case of recursion. J Intell Syst 12(2):113–135Google Scholar
  24. Levy D (2009) Dynamic assessment tools for dynamic constructs: The case of molecular reasoning. Paper presented at the National Association of Research in Science and Technology (NARST) 2009, Garden Grove, CAGoogle Scholar
  25. Levy D, Tinker R (2008) Links between dynamic representations of atomic-scale phenomena and molecular reasoning. In: Eshet-Alkalai Y, Caspi A, Geri N (eds) Proceedings of the Chais conference on instructional technologies research 2008: Learning in the technological era. The Open University of Israel, Raanana, Israel, pp 66–70Google Scholar
  26. Linn MC, Chiu JL (2011) Combining learning and assessment to improve science education. Res Pract Assess 5:5–14Google Scholar
  27. Linn MC, Eylon B-S (2006) Science education: integrating views of learning and instruction. In: Alexander PA, Winne PH (eds) Handbook of educational psychology, 2nd edn. Erlbaum, Mahwah, NJ, pp 511–544Google Scholar
  28. Linn MC, Eylon B-S (2011) Science learning and instruction: taking advantage of technology to promote knowledge integration. Routledge, New York, NYGoogle Scholar
  29. Linn MC, Clark D, Slotta JD (2003) WISE design for knowledge integration. Sci Educat 87:517–538CrossRefGoogle Scholar
  30. Linn MC, Davis EA, Bell P (2004) Inquiry and technology. In: Linn MC, Davis EA, Bell P (eds) Internet environments for science education. Lawrence Erlbaum Associates, Mahwah, NJ, pp 3–28Google Scholar
  31. Liu JL, Linn MC (2011) Knowledge integration and Wise engineering. J Pre Coll Eng Educat Res 1(1):1–14Google Scholar
  32. Lowe R (2004) Interrogation of a dynamic visualization during learning. Learn Instr 14(3):257–274CrossRefGoogle Scholar
  33. Mazur E (2011) The scientific approach to teaching: research as a basis for course design. Keynote presentation at the international computing education research workshop (ICER 2011), Providence, RI. Retrieved September 26, 2011 from http://mazur.harvard.edu/sentFiles/MazurTalk_1712.pdf
  34. Meir E, Perry J, Stal D, Maruka S, Klopfer E (2005) How effective are simulated molecular-level experiments for teaching diffusion and osmosis? Cell Biol Educat 4:235–248CrossRefGoogle Scholar
  35. Michalchik V, Rosenquist A, Kozma R, Kreikemeier P, Schank P, Coppola B (2008) Representational resources for constructing shared understandings in the high school chemistry classroom. In: Gilbert J, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education. Springer, New YorkGoogle Scholar
  36. Narayanan NH, Hegarty M (2002) Multimedia design for communication of dynamic information. Int J Hum Comput Stud 57(4):279–315CrossRefGoogle Scholar
  37. Pallant A, Tinker R (2004) Reasoning with atomic-scale molecular dynamic models. J Sci Educ Technol 13(1):51–66CrossRefGoogle Scholar
  38. Panoff R (2009) Simulations deepen scientific learning. ASCD Express 4(19). Retrieved Aug 25, 2012 from http://www.ascd.org/ascd_express/vol4/419_panoff.aspx
  39. Prince M (2004) Does active learning work? A review of the research. J Eng Educ 93(3):223–231CrossRefGoogle Scholar
  40. Rowell PM (2002) Peer interactions in shared technological activity: a study of participation. Int J Technol Des Educ 12:1–22CrossRefGoogle Scholar
  41. Rundgren C-JA, Tibell L (2010) Critical features of visualizations of transport through the cell membrane: an empirical study of upper secondary and tertiary students’ meaning-making of a still image and an animation. Int J Sci Math Educat 8(2):223–246CrossRefGoogle Scholar
  42. Russell J, Kozma R, Zohdy M, Susskind T, Becker D, Russell C (2000) SMV: Chem (Simultaneous Multiple Representations in Chemistry) [software]. John Wiley, New YorkGoogle Scholar
  43. Sampson V, Clark D (2011) A comparison of the collaborative scientific argumentation practices in two high and two low performing groups. Res Sci Educat 41(1):63–97CrossRefGoogle Scholar
  44. Schmidt H-J, Kaufman B, Treagust DF (2009) Students’ understanding of boiling points and intermolecular forces. Chem Educat Res Pract 4(10):265–272CrossRefGoogle Scholar
  45. Scott P (1998) Teacher talk and meaning making in science classrooms: a Vygotskian analysis and review. Stud Sci Educat 32(1):45–80CrossRefGoogle Scholar
  46. Sengupta P, Wilensky U (2009) Learning electricity with NIELS: thinking with electrons and thinking in levels. Int J Comput Math Learn 14(1):21–50CrossRefGoogle Scholar
  47. Shipley E, Moher T (2008) Instructional framing for nanoscale self-assembly design in middle school: a pilot study. Paper presented at the annual meeting of the American Educational Research Association, March 24–28, 2008Google Scholar
  48. Slotta JD, Linn MC (2009) WISE science: web-based inquiry in the classroom. Teachers College Press, New YorkGoogle Scholar
  49. Slusser R, Erickson RJ (2006) Group quizzes: an extension of the collaborative learning process. Teach Sociol 34(3):249–262CrossRefGoogle Scholar
  50. Smith CL, Wiser M, Anderson CW, Krajcik J, Coppola B (2004) Implications of research on children’s learning for assessment: matter and atomic molecular theory. Paper commissioned by the committee on test design for K-12 science achievement, Center for Education, National Research CouncilGoogle Scholar
  51. Smith CL, Wiser M, Anderson CW, Krajcik J (2006) Implications of research on children’s learning for standards and assessment: a proposed learning progression for matter and the atomic-molecular theory. Meas Interdiscip Res Perspect 4(1&2):1–98CrossRefGoogle Scholar
  52. Springer L, Stanne M, Donovan S (1999) Effects of small-group learning on undergraduates in science, mathematics, engineering and technology: a meta-analysis. Rev Educat Res 69(1):21–52CrossRefGoogle Scholar
  53. Staudt C (2008) Alternative assessments with snapshots. Spring 2008 @Concord Newsletter. Available online at http://www.concord.org/publications/newsletter/2008-spring/
  54. Stieff M (2005) Connected chemistry—a novel modeling environment for the chemistry classroom. J Chem Educ 82(3):489–493CrossRefGoogle Scholar
  55. Strauss AL, Corbin J (1990) Basics of qualitative research. Grounded theory procedures and techniques. Sage, Newbury Park, CAGoogle Scholar
  56. Sutton C (1996) The scientific model as a form of speech. In: Welford G, Osborne J, Scott P (eds) Research in science education in Europe. Falmer Press, London, pp 143–152Google Scholar
  57. Tinker R, Xie Q (2008) Applying computational science to education: the molecular workbench paradigm. Comput Sci Eng 10(5):24–27CrossRefGoogle Scholar
  58. Tinker R, Berenfeld B, Tinker B (1999) HOMS: hands on molecular science: annual report to the national science foundation (REC-9980620)Google Scholar
  59. Tinker R, Berenfeld B, Tinker B (2000) Molecular workbench: annual report to the national science foundation (REC-9813485)Google Scholar
  60. Vosniadou S (2010) Instructional considerations in the use of external representations: the distinction between perceptually based depictions and pictures that represent conceptual models. In: Verschaffel L, de Corte E, de Jong T, Elen J (eds) Use of representations in reasoning and problem solving: analysis and improvement. Routledge, London, pp 36–54Google Scholar
  61. Wieman CE, Adams WK, Perkins KK (2008) PhET: simulations that enhance learning. Science 322(5902):682–683CrossRefGoogle Scholar
  62. Wilensky U, Hazzard E, Froemke R (1999) GasLab—an extensible modeling toolkit for exploring statistical mechanics. Paper presented at the seventh annual European Logo conference EUROLOGO 99. Sofia, BulgariaGoogle Scholar
  63. Wouters P, Paas F, Van Merriënboer JJG (2008) How to optimize learning from animated models: a review of guidelines based on cognitive load. Rev Educat Res 78:645–675CrossRefGoogle Scholar
  64. Xie Q, Lee H-S (2012) A visual approach to nanotechnology education. Int J Eng Educat 28(5):1006–1018Google Scholar
  65. Xie Q, Pallant A (2011) The molecular workbench software: an innovative dynamic modeling tool for nanoscience education. In Khine MS, Saleh IM (eds) Models and modeling: cognitive tools for scientific enquiry. Springer, New York, 121–132Google Scholar
  66. Xie Q, Tinker RF (2006) Molecular dynamics simulations of chemical reactions for use in education. J Chem Educ 83:77–83CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Kibbutzim College of Education, Technology and the ArtsTel AvivIsrael

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