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Cited by 132Science Education and PedagogyVisual and Cognitive Learning ProcessesExperimental Learning in Engineering

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Making the invisible visible: enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation

Olympiou, Georgios, Zacharias, Zacharia, deJong, Ton·2013·Instructional Science
DOI 10.1007/s11251-012-9245-2
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  1. Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33, 131–152. · DOI
  2. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183–198. · DOI · Paper page
  3. Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11, 25–61. · DOI
  4. Ainsworth, S., & Van Labeke, N. (2001). A conceptual framework for designing and evaluating multirepresentational learning environments. Paper presented at the 9th EARLI conference, Fribourg.
  5. Ainsworth, S., & van Labeke, N. (2004). Multiple forms of dynamic representation. Learning and Instruction, 14, 241–255. · DOI
  6. Alessi, S. M., & Trollip, S. R. (1991). Computer-based instruction: Methods and development (2nd ed.). Englewood Cliffs: Prentice-Hall.
  7. Bodemer, D., Ploetzner, R., Bruchmüller, K., & Häcker, S. (2005). Supporting learning with interactive multimedia through active integration of representations. Instructional Science, 33, 73–95. · DOI
  8. Buckley, B. C. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education, 22, 895–935. · DOI
  9. Chang, K.-E. C., Chen, Y. L., Lin, H. Y., & Sung, Y. T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51, 1486–1498. · DOI
  10. ChanLin, L. (1999). Visual treatment for different prior knowledge. International Journal of Instructional Media, 26, 213–219.
  11. Clement, J. J., & Steinberg, M. S. (2002). Step-wise evolution of mental models of electric circuits: A “learning-aloud” case study. The Journal of the Learning Sciences, 11, 389–452. · DOI
  12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  13. Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge: Harvard University Press.
  14. Cook, M. P. (2006). Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Science Education, 90, 1073–1091. · DOI · Paper page
  15. de Jong, T., Ainsworth, S., Dobson, M., van der Hulst, A., Levonen, J., Reimann, P., et al. (1998). Acquiring knowledge in science and mathematics: The use of multiple representations in technology based learning environments. In M. van Someren, P. Reimann, H. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 9–41). Oxford: Elsevier Science.
  16. Deslauriers, L., & Wieman, C. E. (2011). Learning and retention of quantum concepts with different teaching methods. Physical Review Special Topics - Physics Education Research, 7, 010101. · DOI
  17. Duit, R., & Glynn, S. (1996). Mental modelling. In G. Welford, J. Osborne, & P. Scott (Eds.), Research in Science Education in Europe: Current Issues and Themes. London: Falmer.
  18. Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., Reid, S., & LeMaster, R. (2005). When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physical Review Special Topics—Physics Education Research, 1, 1–8. · DOI · Paper page
  19. Frederiksen, J. R., White, B. Y., & Gutwill, J. (1999). Dynamic mental models in learning science: The importance of constructing derivational linkages among models. Journal of Research in Science Teaching, 36, 806–836. · DOI
  20. Gobet, F. (2005). Chunking models of expertise: Implications for education. Applied Cognitive Psychology, 19, 183–204. · DOI
  21. Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13, 239–244. · DOI
  22. Greca, I. M., & Moreira, M. A. (2002). Mental, physical, and mathematical models in the teaching and learning of physics. Science Education, 86, 106–121. · DOI
  23. Halloun, I. A. (2007). Mediated modeling in science education. Science & Education, 16, 653–697. · DOI
  24. Hatzikraniotis, E., Bisdikian, G., Barbas, A., Psillos, D. (2007). Optilab: Design and development of an integrated virtual laboratory for teaching optics. Proceedings of the 7th International Conference on Computer Based Learning in Science, CBLIS, Crete, Greece.
  25. Hestenes, D. (1992). Modeling games in the newtonian world. American Journal of Physics, 60, 732–748. · DOI
  26. Hogan, K., Nastasi, B. K., & Pressley, M. (2000). Discourse patterns and collaborative scientific reasoning in peer and teacher-guided discussions. Cognition and Instruction, 17, 379–432. · DOI · Paper page
  27. Huk, T., Steinke, M., & Floto, C. (2010). The educational value of visual cues and 3D-representational format in a computer animation under restricted and realistic conditions. Instructional Science, 38, 455–469. · DOI
  28. Huppert, J., Lomask, S. M., & Lazarowitz, R. (2002). Computer simulations in the high school: Students’ cognitive stages, science process skills and academic achievement in microbiology. International Journal of Science Education, 24, 803–821. · DOI
  29. Ingerman, A., Linder, C., & Marshall, D. (2009). The learners’ experience of variation: Following students’ threads of learning physics in computer simulation sessions. Instructional Science, 37, 273–292. · DOI
  30. Jaakkola, T., Nurmi, S., & Veermans, K. (2010). A comparison of students’ conceptual understanding of electric circuits in simulation only and simulation-laboratory contexts. Journal of Research in Science Teaching, 48, 71–93. · DOI
  31. Johnson, M. A., & Lawson, A. E. (1998). What are the relative effects of reasoning ability and prior knowledge on biology achievement in expository and inquiry classes? Journal of Research in Science Teaching, 35, 89–103. · DOI
  32. Kozma, R., Russell, J., Jones, T., Marx, N., & Davis, J. (1996). The use of multiple, linked representations to facilitate science understanding. In S. Vosniadou, E. De Corte, R. Glaser, & H. Mandl (Eds.), International perspectives on the design of technology-supported learning environments (pp. 41–60). Mahwah, NJ: Lawrence Earlbaum Associates.
  33. Limniou, M., Papadopoulos, N., & Whitehead, C. (2009). Integration of simulation into pre-laboratory chemical course: Computer cluster versus WebCT. Computers & Education, 52, 45–52. · DOI · Paper page
  34. Liu, H.-C., Andre, T., & Greenbowe, T. (2008). The impact of learner’s prior knowledge on their use of chemistry computer simulations: A case study. Journal of Science Education and Technology, 17, 466–482. · DOI
  35. Martinez, G., Naranjo, F. L., Perez, A. L., Suero, M. I., & Pardo, P. J. (2011). Comparative study of the effectiveness of three learning environments: Hyper-realistic virtual simulations, traditional schematic simulations and traditional laboratory. Physical Review Special Topics: Physics Education Research, 7(2), 020111-1–020111-12. · DOI
  36. Marušić, M., & Sliško, J. (2012). Influence of three different methods of teaching physics on the gain in students’ development of reasoning. International Journal of Science Education, 34(2), 301–326. · DOI
  37. Mayer, R. E., & Sims, V. K. (1994). For whom is a picture worth 1000 words e Extensions of a dual-coding theory of multimedia learning. Journal of Educational Psychology, 86, 389–401. · DOI · Paper page
  38. McDermott, L. C., & Shaffer, P. S. (1992). Research as a guide for curriculum development: An example from introductory electricity, Part I: Investigation of student understanding. American Journal of Physics, 60, 994–1003. · DOI
  39. McDermott and the Physics Education Group at the University of Washington. (1996). Physics by inquiry (Volume II).. New York: Wiley.
  40. Mikropoulos, T. A., & Natsis, A. (2011). Educational virtual environments: A ten-year review of empirical research (1999–2009). Computers & Education, 56, 769–780. · DOI
  41. Minogue, J., Gail Jones, M., Broadwell, B., & Oppewall, T. (2006). The impact of haptic augmentation on middle school students’ conceptions of the animal cell. Virtual Reality, 10, 293–305. · DOI
  42. Monaghan, J. M., & Clement, J. (1999). Algorithms, visualization, and mental models: High school students’ interactions with a relative motion simulation. Journal of Science Education and Technology, 9(4), 311–325. · DOI
  43. Olympiou, G., & Zacharia, Z. (2012). Blending physical and virtual manipulatives: An effort to improve students’ conceptual understanding through science laboratory experimentation. Science Education, 96(1), 21–47. · DOI
  44. Penner, D. E., Lehrer, R., & Schauble, L. (1998). From physical models to biomechanical systems: A design-based modeling approach. Journal of the Learning Sciences, 7(3&4), 429–449. · DOI
  45. Petre, M., Blackwell, A. F., & Green, T. R. G. (1998). Cognitive questions in software visualization. In J. Stasko, J. Domingue, M. Brown, & B. Price (Eds.), Software visualization: Programming as a multi-media experience (pp. 453–480). Cambridge, MA: MIT Press.
  46. Ploetzner, R., & Lowe, R. (2004). Dynamic visualizations and learning. Learning and Instruction, 14, 235–241. · DOI
  47. Pozzer-Ardenghi, & Roth, W.-M. (2005). Making sense of photographs. Science Education, 89, 219–241. · DOI
  48. Rapp, D. N. (2005). Mental models: Theoretical issues for visualizations in science education. In J. K. Gilbert (Ed.), Visualization in Science Education (pp. 43–60). Dordrecht: Springer. · DOI
  49. Redish, E. F., & Steinberg, R. N. (1999). Teaching physics: Figuring out what works. Physics Today, 52, 24–30. · DOI
  50. Ronen, M., & Eliahu, M. (2000). Simulation—A bridge between theory and reality: The case of electric circuits. Journal of Computer Assisted Learning, 16, 14–26. · DOI
  51. Roth, W. M., & Roychoudhury, A. (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30, 127–152. · DOI
  52. Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K., & Irvin, P. S. (2011). Student learning in science simulations: Design features that promote learning gains. Journal of Research in Science Teaching, 48, 1050–1078. · DOI
  53. Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 49–69). Cambridge: Cambridge University Press. · DOI
  54. Schnotz, W., & Kürschner, C. (2008). External and internal representations in the acquisition and use of knowledge: Visualization effects on mental model construction. Instructional Science, 36, 175–190. · DOI
  55. Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13, 227–237. · DOI
  56. Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34, 1337–1370. · DOI · Paper page
  57. Trundle, K. C., & Bell, R. L. (2010). The use of a computer simulation to promote conceptual change: A quasi-experimental study. Computers & Education, 54, 1078–1088. · DOI
  58. Tsui, C.-Y., & Treagust, D. F. (2003). Genetics reasoning with multiple external representations. Research in Science Education, 33, 111–135. · DOI
  59. van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16, 199–212. · DOI
  60. Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4, 45–69. · DOI
  61. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge: Harvard University Press.
  62. Wells, G. (1999). Dialogic inquiry. Towards a sociocultural practice and theory of education. Cambridge, MA: Cambridge University Press. · DOI
  63. Wu, H.-K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842. · DOI
  64. Zacharia, Z. C. (2005). The impact of interactive computer simulations on the nature and quality of postgraduate science teachers’ explanations in physics. International Journal of Science Education, 27, 1741–1767. · DOI
  65. Zacharia, Z. C. (2007). Comparing and combining real and virtual experimentation: An effort to enhance students’ conceptual understanding of electric circuits. Journal of Computer Assisted Learning, 23, 120–132. · DOI
  66. Zacharia, Z. C., & Anderson, O. R. (2003). The effects of an interactive computer-based simulation prior to performing a laboratory inquiry-based experiment on students’ conceptual understanding of physics. American Journal of Physics, 71(6), 618–629. · DOI
  67. Zacharia, Z. C., & Constantinou, C. P. (2008). Comparing the influence of physical and virtual manipulatives in the context of the Physics by Inquiry curriculum: The case of undergraduate students’ conceptual understanding of heat and temperature. American Journal of Physics, 76, 425–430. · DOI
  68. Zacharia, Z. C., & Olympiou, G. (2011). Physical versus virtual manipulative experimentation in physics learning. Learning and Instruction, 21, 317–331. · DOI
  69. Zacharia, Z. C., Olympiou, G., & Papaevripidou, M. (2008). Effects of experimenting with physical and virtual manipulatives on students’ conceptual understanding in heat and temperature. Journal of Research in Science Teaching, 45, 1021–1035. · DOI

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Metadata

Authors
Olympiou, G., Zacharias, Z., & deJong, T.
Affiliations
University of Cyprus (CY)
Venue
Instructional Science
Publisher
Springer Science and Business Media LLC
Published
2012-06-07
Volume
41
Issue
3
Pages
575-596
ISSN
  • 0020-4277 (ISSN-L)
  • 1573-1952
DOI
10.1007/s11251-012-9245-2
OpenAlex
W2144248998
Cited by
132
References
2
Citations by year
2026: 8 · 2025: 4 · 2024: 18 · 2023: 15 · 2022: 9 · 2021: 14 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 5 · 2016: 6 · 2015: 11 · 2014: 10 · 2013: 5
FWCI
40.42
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • Instructional Science (publishedVersion)
  • University of Twente Research Information (publishedVersion)
  • The Library of Electronic Cyprus Thematic Organized Collections (LYKYTHOS) (University of Cyprus) (submittedVersion)
  • University of Twente Research Information (submittedVersion)
Topics
  • Social Sciences › Education › Science Education and Pedagogy
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Social Sciences › Education › Experimental Learning in Engineering
Keywords
abstract representation, concrete representations, conceptual understanding, physics simulation, light and color, prior knowledge, inquiry-based learning

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