Journal of Science Education and Technology

, Volume 25, Issue 5, pp 806–823 | Cite as

Order Matters: Sequencing Scale-Realistic Versus Simplified Models to Improve Science Learning

  • Chen Chen
  • Matthew H. Schneps
  • Gerhard Sonnert


Teachers choosing between different models to facilitate students’ understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning based on multiple models, thus giving rise to questions about the order of presentation. Using disjoint individual growth modeling to examine the learning of astronomical concepts using a simulation of the solar system on tablets for 152 high school students (age 15), the authors detect both a model effect and an order effect in the use of the Orrery, a simplified model that exaggerates the scale relationships, and the True-to-scale, a proportional model that more accurately represents the realistic scale relationships. Specifically, earlier exposure to the simplified model resulted in diminution of the conceptual gain from the subsequent realistic model, but the realistic model did not impede learning from the following simplified model.


Order effect Model-based learning Misconception STEM education 



This work was supported in part from funds from a George E. Burch Foundation fellowship to the Smithsonian Institution for MHS. We thank the faculty members of the Bedford High School Science department for their contributions to this experiment, Jon Sills for encouraging this research, and Robert Speiser for insights and discussion. Assessment items were generated with funding from the National Aeronautics and Space Administration’s Structure and Evolution of the Universe Forum (NCC5-706) and from the National Science Foundation grant for MOSART (Misconceptions-Oriented Standards-Based Assessment Resource for Teachers, NSF HER 0412382). Any opinions, findings, 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 Smithsonian Institution, or the National Aeronautics and Space Administration.


  1. Aleixandre MPJ (1994) Teaching evolution and natural selection: a look at textbooks and teachers. J Res Sci Teach 31(5):519–535CrossRefGoogle Scholar
  2. Anderson D, Fisher K, Norman G (2002) Development and evaluation of the conceptual inventory of natural selection. J Res Sci Teach 39(10):952–978CrossRefGoogle Scholar
  3. Ausubel DP (1969) Some psychological and education limitations of learning by discovery. In: Andersen HO (ed) Readings in science education for the secondary school. Macmillan Company, New York, New York, pp 97–113Google Scholar
  4. Bartlett FC, Burt C (1933) Remembering: a study in experimental and social psychology. Br J Educ Psychol 3(2):187–192CrossRefGoogle Scholar
  5. Baun DA, Smith SD, Donovan SS (2005) The tree-thinking challenge. Science 310(5750):979–980CrossRefGoogle Scholar
  6. Berland LK, McNeill KL (2010) A learning progression for scientific argumentation: understanding student work and designing supportive instructional contexts. Sci Educ 94(5):765–793CrossRefGoogle Scholar
  7. Boroditsky L (2000) Metaphoric structuring: understanding time through spatial metaphors. Cognition 75(1):1–28CrossRefGoogle Scholar
  8. Boudreaux H, Bible P, Cruz-Neira C, Parham T, Cervato C, Gallus W, Stelling P (2009) V-volcano: addressing students’ misconceptions in earth sciences learning through virtual reality simulations. In International symposium on visual computing. Springer, Berlin Heidelberg, pp 1009–1018Google Scholar
  9. Box GE, Draper NR (1987) Empirical model-building and response surfaces, vol 424. Wiley, New YorkGoogle Scholar
  10. Boyes E (1988) Catastrophic misconceptions in science education. Phys Educ 23(2):105CrossRefGoogle Scholar
  11. Brewer WF, Nakamura GV (1984) The nature and functions of schemas (Technical report no. 325). University of Illinois, Champaign-UrbanaGoogle Scholar
  12. Briggs D, Alonzo A, Schwab C, Wilson M (2006) Diagnostic assessment with ordered multiple-choice items. Educ Assess 11(1):33–63CrossRefGoogle Scholar
  13. Cameron L (2003) Metaphor in educational discourse. A&C BlackGoogle Scholar
  14. Carey S (2000) Science education as conceptual change. J Appl Dev Psychol 21(1):13–19CrossRefGoogle Scholar
  15. Chater N, Lyon K, Myers T (1990) Why are conjunctive categories overextended? J Exp Psychol Learn Mem Cogn 16(3):497CrossRefGoogle Scholar
  16. Chew SL (2006) Seldom in doubt but often wrong: addressing tenacious student misconceptions. In: Chew SL, Dunn DL (eds) Best practices for teaching introduction to psychology. Erlbaum, Mahway, pp 211–223Google Scholar
  17. Clement JJ (2008) Student/teacher co-construction of visualizable models in large group discussion. In: Clement J, Rea-Ramirez MA (eds) Model based learning and instruction in science. Springer, Dordrecht, pp 11–22CrossRefGoogle Scholar
  18. Collins A (1989) Cognitive apprenticeship and instructional technology (Technical report no. 474). University of Illinois, Champaign-UrbanaGoogle Scholar
  19. Coopmans C, Vertesi J, Lynch, ME, Woolgar S (2014) Representation in scientific practice revisited. MIT PressGoogle Scholar
  20. Corcoran T, Mosher FA, Rogat A (2009) Learning progressions in science: an evidence-based approach to reform. CPRE Research Reports.
  21. de Jong T, Linn MC, Zacharia ZC (2013) Physical and virtual laboratories in science and engineering education. Science 340(6130):305–308CrossRefGoogle Scholar
  22. Digirolamo GJ, Hintzman DL (1997) First impressions are lasting impressions: a primacy effect in memory for repetitions. Psychon Bull Rev 4(1):121–124CrossRefGoogle Scholar
  23. Diomidous M, Verginis I, Mantas J (1998) The construction of a simulation-based system for the development of powerful and realistic models and practicals for healthcare professionals. IEEE Trans Inf Technol Biomed 2(3):174–182CrossRefGoogle Scholar
  24. Dodick J, Orion N (2003) Cognitive factors affecting student understanding of geologic time. J Res Sci Teach 40(4):415–442CrossRefGoogle Scholar
  25. Driver R, Easley J (1978) Pupils and paradigms: a review of literature related to concept development in adolescent science students. Stud Sci Educ 5(1):61–84CrossRefGoogle Scholar
  26. Duncan RG, Gotwals AW (2015) A tale of two progressions: on the benefits of careful comparisons. Sci Educ 99(3):410–416CrossRefGoogle Scholar
  27. Duncan RG, Castro-Faix M, Choi J (2016) Informing a Learning Progression in Genetics: which Should BE Taught First, Mendelian Inheritance or the Central Dogma of Molecular Biology?. Int J Sci Math Educ, 14(3):445–472CrossRefGoogle Scholar
  28. Duncan RG, Rogat AD, Yarden A (2009) A learning progression for deepening students’ understandings of modern genetics across the 5th–10th grades. J Res Sci Teach 46(6):655–674CrossRefGoogle Scholar
  29. Duschl R, Maeng S, Sezen A (2011) Learning progressions and teaching sequences: a review and analysis. Stud Sci Educ 47(2):123–182CrossRefGoogle Scholar
  30. Elmesky R (2013) Building capacity in understanding foundational biology concepts: a K-12 learning progression in genetics informed by research on children’s thinking and learning. Res Sci Educ 43(3):1155–1175CrossRefGoogle Scholar
  31. Ford DN, McCormack DE (2000) Effects of time scale focus on system understanding in decision support systems. Simul Gaming 31(3):309–330CrossRefGoogle Scholar
  32. Francoeur E (1997) The forgotten tool: the design and use of molecular models. Soc Stud Sci 27(1):7–40CrossRefGoogle Scholar
  33. Frederiksen JR, White BY, Gutwill J (1999) Dynamic mental models in learning science: the importance of constructing derivational linkages among models. J Res Sci Teach 36(7):806–836CrossRefGoogle Scholar
  34. Gagne RM (1965) The conditions of learning. Holt, Rinehart and Winston, New YorkGoogle Scholar
  35. Garner WA, Strohmer DC, Langford CA, Boas GJ (1994) Diagnostic and treatment overshadowing bias across disabilities: are rehabilitation professionals immune? J Appl Rehabil Couns 25:33–37Google Scholar
  36. Gentner D (1983) Structure-mapping: a theoretical framework for analogy. Cogn Sci 7(2):155–170CrossRefGoogle Scholar
  37. Gentner D, Wolff P (1997) Alignment in the processing of metaphor. J Mem Lang 37(3):331–355CrossRefGoogle Scholar
  38. Gentner D, Wolff P (2000) Metaphor and knowledge change. In: Dietrich E, Markman A (eds) Cognitive dynamics: conceptual change in humans and machines. Erlbaum, Mahwah, pp 295–342Google Scholar
  39. Giere RN (1999) Using models to represent reality. Model-based reasoning in scientific discovery. Springer, New York, pp 41–57CrossRefGoogle Scholar
  40. Giere RN (2004) How models are used to represent reality. Philos Sci 71(5):742–752CrossRefGoogle Scholar
  41. Gilbert JK (2004) Models and modelling: routes to more authentic science education. Int J Sci Math Educ 2(2):115–130CrossRefGoogle Scholar
  42. Gilbert JK, Boulter C, Rutherford M (1998) Models in explanations, Part 1: horses for courses? Int J Sci Educ 20(1):83–97CrossRefGoogle Scholar
  43. Goldsmith DW (2003) The great clade race: presenting cladistic thinking to biology majors and general science students. Am Biol Teacher 65:679–682CrossRefGoogle Scholar
  44. Goldsmith L, Schloss P (1984) Diagnostic overshadowing among learning-disabled and hearing-impaired learners with an apparent secondary diagnosis of behavior disorders. Int J Partial Hosp 2:209–217Google Scholar
  45. Halloun IA, Hestenes D (1985) The initial knowledge state of college physics students. Am J Phys 53(1):1043–1055CrossRefGoogle Scholar
  46. Haste H (2001) Ambiguity, autonomy and agency: psychological challenges to new competence. In: Rychen D, Salganik L (eds) Defining and selecting key competencies. Hogrefe & Huber, Kirkland, pp 93–120Google Scholar
  47. Hattie J, Yates GC (2013) Visible learning and the science of how we learn. Routledge, New YorkGoogle Scholar
  48. Hayward M (2009) Earth science misconceptions. Teach Earth Sci 34(2):34Google Scholar
  49. Hewson PW, Hewson MGB (1984) The role of conceptual conflict in conceptual change and the design of science instruction. Instr Sci 13(1):1–13CrossRefGoogle Scholar
  50. Ingham AM, Gilbert JK (1991) The use of analogue models by students of chemistry at higher education level. Int J Sci Educ 13(2):193–202CrossRefGoogle Scholar
  51. Justi R, Gilbert JK, Ferreira PF (2009) The application of a “model of modelling” to illustrate the importance of metavisualisation in respect of the three types of representation. Multiple representations in chemical education. Springer, Dordrecht, pp 285–307CrossRefGoogle Scholar
  52. Keeley JW, DeLao CS, Kirk CL (2013) The commutative property in comorbid diagnosis does A+B = B+A? Clin Psychol Sci 1(1):16–29CrossRefGoogle Scholar
  53. Kharitonova M, Munakata Y (2011) The role of representations in executive function: investigating a developmental link between flexibility and abstraction. Front Psychol. doi: 10.3389/fpsyg.2011.00347 Google Scholar
  54. Kozma R (2003) The material features of multiple representations and their cognitive and social affordances for science understanding. Learn Instr 13(2):205–226CrossRefGoogle Scholar
  55. Lakoff G, Johnson M (1980) Metaphors we live by. University of Chicago Press, ChicagoGoogle Scholar
  56. Larkin J, Chabay RW (1989) Research on teaching scientific thinking: implications for computer-based instruction, in toward the thinking curriculum: current cognitive research. ASCD, AlexandriaGoogle Scholar
  57. Latour B, Strum SC (1986) Human social origins: Oh please, tell us another story. J Soc Biol Struct 9(2):169–187CrossRefGoogle Scholar
  58. Leatherdale WH (1974) The role of analogy, model and metaphor in science. North-Holland Publishing Company, AmsterdamGoogle Scholar
  59. Lehrer R, Schauble L (2012) Seeding evolutionary thinking by engaging children in modeling its foundations. Sci Educ 96(4):701–724CrossRefGoogle Scholar
  60. Levitan GW, Reiss S (1983) Generality of diagnostic overshadowing across disciplines. Appl Res Mental Retard 4(1):59–64CrossRefGoogle Scholar
  61. Linenberger KJ, Bretz SL (2012) Generating cognitive dissonance in student interviews through multiple representations. Chem Educ Res Pract 13(3):172–178CrossRefGoogle Scholar
  62. Low G (2008) Metaphor and education. In: The Cambridge handbook of metaphor and thought, pp 212–231Google Scholar
  63. Lynch M, Woolgar S (eds) (1990) Representation in scientific practice, p 1. Cambridge, MA: MIT pressGoogle Scholar
  64. Magnani L, Nersessian N, Thagard P (eds) (2012) Model-based reasoning in scientific discovery. Springer Science & Business Media, DordrechtGoogle Scholar
  65. Masson S, Potvin P, Riopel M, Foisy LMB (2014) Differences in brain activation between novices and experts in science during a task involving a common misconception in electricity. Mind Brain Educ 8(1):44–55CrossRefGoogle Scholar
  66. Mayer RE (1976) Some conditions of meaningful learning for computer programming: advance organizers and subject control of frame order. J Educ Psychol 68(2):143CrossRefGoogle Scholar
  67. Meir E, Herron JC, Maruca S, Stal D, Kingsolver J (2005) EvoBeaker 1.0. SimBiotic Software, Ithaca.
  68. Meir E, Perry J, Herron JC, Kingsolver J (2007) College students’ misconceptions about evolutionary trees. Am Biol Teacher 69(7):e71–e76CrossRefGoogle Scholar
  69. Meyer JH, Land R (2005) Threshold concepts and troublesome knowledge (2): epistemological considerations and a conceptual framework for teaching and learning. High Educ 49(3):373–388CrossRefGoogle Scholar
  70. Meyer J, Land R (2013) Overcoming barriers to student understanding: threshold concepts and troublesome knowledge. Routledge, New YorkGoogle Scholar
  71. Meyer JHF, Shanahan M (2003). The troublesome nature of a threshold concepts in economics. Paper presented to the 10th Conference of the European Association fro Research on Learning an Instruction (EARLI), PadovaGoogle Scholar
  72. Michael J (2007) What makes physiology hard for students to learn? Results of a faculty survey. Adv Physiol Educ 31(1):34–40CrossRefGoogle Scholar
  73. Miller BW, Brewer WF (2010) Misconceptions of astronomical distances. Int J Sci Educ 32(12):1549–1560CrossRefGoogle Scholar
  74. Miller JK, Westerman DL, Lloyd ME (2004) Are first impressions lasting impressions? An exploration of the generality of the primacy effect in memory for repetitions. Memory Cogn 32(8):1305–1315CrossRefGoogle Scholar
  75. Mintzes JJ, Wandersee JH, Novak JD (2005) Assessing science understanding: a human constructivist view. Educational psychology. Academic Press, Cambridge, MACrossRefGoogle Scholar
  76. Mohan L, Chen J, Anderson CW (2009) Developing a multi-year learning progression for carbon cycling in socio-ecological systems. J Res Sci Teach 46(6):675–698CrossRefGoogle Scholar
  77. Morton JP, Doran DA, MacLaren DP (2008) Common student misconceptions in exercise physiology and biochemistry. Adv Physiol Educ 32(2):142–146CrossRefGoogle Scholar
  78. Muller DA, Sharma MD, Reimann P (2008) Raising cognitive load with linear multimedia to promote conceptual change. Sci Educ 92(2):278–296CrossRefGoogle Scholar
  79. National Committee on Science Education Standards and Assessment, National Research Council (1996) National science education standards. National Academies Press.
  80. Nersessian N (1984) Faraday to Einstein: constructing meaning in scientific theories. Springer Science & Business MediaGoogle Scholar
  81. Nersessian NJ (1999) Model-based reasoning in conceptual change. In Model-based reasoning in scientific discovery (pp. 5–22). Springer USGoogle Scholar
  82. Nersessian NJ (2002) Maxwell and “the method of physical analogy”: Model-based reasoning, generic abstraction, and conceptual change. Essays in the History and Philosophy of Science and Mathematics, 129–166Google Scholar
  83. Odom AL (1993) Action potentials and biology textbooks: accurate, misconceptions or avoidance? Am Biol Teacher 55(8):468–472CrossRefGoogle Scholar
  84. Perry J, Meir E, Herron JC, Maruca S, Stal D (2008) Evaluating two approaches to helping college students understand evolutionary trees through diagramming tasks. CBE Life Sci Educ 7(2):193–201CrossRefGoogle Scholar
  85. Piaget J (1951) The child’s conception of the world (No. 213). Rowman & LittlefieldGoogle Scholar
  86. Plummer JD, Krajcik J (2010) Building a learning progression for celestial motion: elementary levels from an earth-based perspective. J Res Sci Teach 47(7):768–787CrossRefGoogle Scholar
  87. Posner GJ, Strike KA, Hewson PW, Gertzog WA (1982) Accommodation of a scientific conception: toward a theory of conceptual change. Sci Educ 66(2):211–227CrossRefGoogle Scholar
  88. Raghavan K, Glaser R (1995) Model-based analysis and reasoning in science: the MARS curriculum. Sci Educ 79(1):37–61CrossRefGoogle Scholar
  89. Rea-Ramirez MA (2008) Determining target models and effective learning pathways for developing understanding of biological topics. In: Clement J, Rea-Ramirez MA (eds) Model based learning and instruction in science. Springer, Dordrecht, pp 45–58CrossRefGoogle Scholar
  90. Reigeluth CM (1999) The elaboration theory: guidance for scope and sequence decisions. Instr Design Theories Models: A New Paradigm Instr Theory 2:425–453Google Scholar
  91. Reiss S, Levitan GW, Szyszko J (1982) Emotional disturbance and mental retardation: diagnostic overshadowing. Am J Mental Defic 86:567–574Google Scholar
  92. Rosenblueth A, Wiener N (1945) The role of models in science. Philos Sci 12(4):316–321CrossRefGoogle Scholar
  93. Roumeliotis M (1998) Simulation techniques. Paratiritis, AthensGoogle Scholar
  94. Rouse WB, Morris NM (1986) On looking into the black box: prospects and limits in the search for mental models. Psychol Bull 100(3):349CrossRefGoogle Scholar
  95. Sadler P (1998) Psychometric models of student conceptions in science: reconciling qualitative studies and distractor-driven assessment instruments. J Res Sci Teach 35(3):265–296CrossRefGoogle Scholar
  96. Sadler PM, Coyle H, Miller JL, Cook Smith N, Dussault M, Gould RR (2010) The astronomy and space science concept inventory: development and validation of assessment instruments aligned with the K-12 national science standards. Astron Educ Rev 8:0111Google Scholar
  97. Schneps MH, Sadler PM (1987) A private universe. Pyramid Films, Santa MonicaGoogle Scholar
  98. Schneps MH, Ruel J, Sonnert G, Dussault M, Griffin M, Sadler PM (2014) Conceptualizing astronomical scale: virtual simulations on handheld tablet computers reverse misconceptions. Comput Educ 70:269–280CrossRefGoogle Scholar
  99. Schwarz CV, Reiser BJ, Davis EA, Kenyon L, Achér A, Fortus D et al (2009) Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. J Res Sci Teach 46(6):632–654CrossRefGoogle Scholar
  100. Sfard A (1998) On two metaphors for learning and the dangers of choosing just one. Educ Res 27(2):4–13CrossRefGoogle Scholar
  101. Singer JD, Willett JB (2003) Applied longitudinal data analysis: modeling change and event occurrence. Oxford, New YorkCrossRefGoogle Scholar
  102. Snyder JL (2000) An investigation of the knowledge structures of experts, intermediates and novices in physics. Int J Sci Edu 22(9):979–992CrossRefGoogle Scholar
  103. Snyder HR, Munakata Y (2010) Becoming self-directed: abstract representations support endogenous flexibility in children. Cognition 116(2):155–167CrossRefGoogle Scholar
  104. Spiro RJ, Feltovich PJ, Coulson RL, Anderson DK (1989) Multiple analogies for complex concepts: antidotes for analogy-induced misconception in advanced knowledge acquisition. Cambridge, CambridgeGoogle Scholar
  105. Springer K, Murphy GL (1992) Feature availability in conceptual combination. Psychol Sci 3(2):111–117CrossRefGoogle Scholar
  106. Steinberg MS (2008) Target model sequence and critical learning pathway for an electricity curriculum based on model evolution. In: Clement J, Rea-Ramirez MA (eds) Model based learning and instruction in science. Springer, Dordrecht, pp 79–102CrossRefGoogle Scholar
  107. Steinberg MS, Wainwright CL (1993) Using models to teach electricity: the CASTLE project. Phys Teacher 31:353–357CrossRefGoogle Scholar
  108. Stevens SY, Delgado C, Krajcik JS (2010) Developing a hypothetical multi-dimensional learning progression for the nature of matter. J Res Sci Teach 47(6):687–715CrossRefGoogle Scholar
  109. Van Merrienboer JJ, Sweller J (2005) Cognitive load theory and complex learning: recent developments and future directions. Educ Psychol Rev 17(2):147–177CrossRefGoogle Scholar
  110. von Aufschnaiter C, Rogge C (2010) Misconceptions or missing conceptions. Eurasia J Math Sci Technol Educ 6(1):3–18Google Scholar
  111. Walker G (2013) A cognitive approach to threshold concepts. High Educ 65(2):247–263CrossRefGoogle Scholar
  112. Wilson B, Cole P (1991) A review of cognitive teaching models. Educ Tech Res Dev 39(4):47–64CrossRefGoogle Scholar
  113. Wisniewski EJ (1996) Construal and similarity in conceptual combination. J Mem Lang 35(3):434–453CrossRefGoogle Scholar
  114. Zaitchik D, Iqbal Y, Carey S (2013) The effect of executive function on biological reasoning in young children: an individual differences study. Child Dev 85(1):160–175CrossRefGoogle Scholar
  115. Zander C, Boustedt J, Eckerdal A, McCartney R, Moström JE, Ratcliffe M, Sanders K (2008) Threshold concepts in computer science: a multi-national empirical investigation. In: Land R, Meyer J, Smith J (eds) Threshold concepts within the disciplines. Sense, Rotterdam, pp 105–118Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Harvard Graduate School of EducationCambridgeUSA
  2. 2.University of MassachusettsBostonUSA
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA
  4. 4.Harvard-Smithonian Center for AstrophysicsCambridgeUSA

Personalised recommendations