Scaffolding learning from molecular visualizations

Journal of Research in Science Teaching

Volumn 50, Issue 7, 2013, Pages 858-886

  Chang, Hsin Yi ^a   Linn, Marcia C ^b  

^a NATIONAL KAOHSIUNG NORMAL UNIVERSITY   (Taiwan)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Critique; inquiry; knowledge integration; molecular visualization; Observation; thermodynamics; virtual experiment; Web based Inquiry Science Environment

Indexed keywords

CRITIQUE; INQUIRY; KNOWLEDGE INTEGRATION; MOLECULAR VISUALIZATION; OBSERVATION; VIRTUAL EXPERIMENTS; WEB-BASED INQUIRY SCIENCE ENVIRONMENT;

EXPERIMENTS; SCAFFOLDS; THERMODYNAMICS; VIRTUAL REALITY; VISUALIZATION; WEBSITES;

STUDENTS;

EID: 84881115378     PISSN: 00224308     EISSN: 10982736     Source Type: Journal    
DOI: 10.1002/tea.21089     Document Type: Article

References (85)

1. Ainsworth, S., Prain, V., &, Tytler, R., (2011). Drawing to learn in science. Science, 333, 1096-1097.
2. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York, New York: Oxford University Press, Inc.
3. Ardac, D., &, Akaygun, S., (2004). Effectiveness of multimedia-based instruction that emphasizes representations on students' understanding of chemical change. Journal of Research in Science Teaching, 41, 317-337.
4. Ardac, D., &, Akaygun, S., (2005). Using static and dynamic visuals to represent chemical change at molecular level. International Journal of Science Education, 27, 1269-1298.
5. Barnea, N., &, Dori, Y. J., (1996). Computerized molecular modeling as a tool to improve chemistry teaching. Journal of Chemical Information and Computer Sciences, 36, 629-636.
6. Betrancourt, M., (2005). The animation and interactivity principles in multimedia learning. In, R. E. Mayer, (Ed.), The Cambridge handbook of multimedia learning (pp. 287-296). New York, NY: Cambridge University Press.
7. Bodemer, D., Ploetzner, R., Feuerlein, I., &, Spada, H., (2004). The active integration of information during learning with dynamic and interactive visualizations. Learning and Instruction, 14, 325-341.
8. Chang, H.-Y., Quintana, C., &, Krajcik, J. S., (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94, 73-94.
9. ChanLin, L.-J., (2001). Formats and prior knowledge on learning in a computer-based lesson. Journal of Computer Assisted Learning, 17, 409-419.
10. Chinn, C. A., &, Brewer, W. F., (2001). Models of data: A theory of how people evaluate data. Cognition and Instruction, 19, 323-393.
11. Chiu, J., &, Linn, M. C., (2011). Knowledge integration and WISE engineering. Journal of Pre-college Engineering Education Research, 1 (1), 1-14.
12. Chiu, J., &, Linn, M. C., (2012). The role of self-monitoring in learning chemistry with dynamic visualizations. In, A. Zohar, &, Y. J. Dori, (Eds.), Metacognition in science education (pp. 133-163). Dordrecht, Netherlands: Springer.
13. Clark, D. B., (2006). Longitudinal conceptual change in students' understanding of thermal equilibrium: An examination of the process of conceptual restructuring. Cognition and Instruction, 24, 467-563.
14. Clark, D. B., &, Jorde, D., (2004). Helping students revise disruptive experientially supported ideas in thermodynamics: Computer visualizations and tactile models. Journal of Research in Science Teaching, 41, 1-23.
15. Clark, D. B., &, Sampson, V., (2007). Personally-seeded discussions to scaffold online argumentation. International Journal of Science Education, 29, 253-277.
16. Clark, D. B., &, Sampson, V., (2008). Assessing dialogic argumentation in online environments to relate structure, grounds, and conceptual quality. Journal of Research in Science Teaching, 45, 293-321.
17. Clement, J., (1993). Using bridging analogies and anchoring intuitions to deal with students' preconceptions in physics. Journal of Research in Science Teaching, 30, 1241-1257.
18. Cohen, J., (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
19. Collins, A., Brown, J. S., &, Holum, A., (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 6 (11), 38-46.
20. Cuthbert, A. F., &, Slotta, J. D., (2004). Designing a web-based design curriculum for middle school science: The WISE 'houses in the desert' project. International Journal of Science Education, 26, 821-844.
21. Davis, E., (2003). Prompting middle school science students for productive reflection: Generic and directed prompts. The Journal of the Learning Science, 12, 91-142.
22. de Jong, T., &, van Joolingen, W. R., (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179-201.
23. diSessa, A. A., (2002). Students' criteria for representational adequacy. In, K. Gravemeijer, R. Lehrer, B. Van Oers, &, L. Verschaffel, (Eds.), Symbolizing, modeling and tool use in mathematics education (pp. 105-129). Boston, Massachusetts: Kluwer Academic Publishers.
24. diSessa, A. A., (2004). Meta-representation: Native competence and targets for instruction. Cognition and Instruction, 22, 293-331.
25. Driver, R., Squires, A., Rushworth, P., &, Wood-Robinson, V., (1994). Making sense of secondary science-Research into children's ideas. London: Routledge.
26. Frailich, M., Kesner, M., &, Hofstein, A., (2009). Enhancing students' understanding of the concept of "chemical bonding" by using activities provided on an interactive website. Journal of Research in Science Teaching, 46, 289-310.
27. Gabel, D. L., (1993). Use of the particle nature of matter in developing conceptual understanding. Journal of Chemical Education, 70, 193-194.
28. Gilbert, J. K., (2008). Visualization: An emergent field of practice and enquiry in science education. In, J. K. Gilbert, (Ed.), Visualization: Theory and practice in science education (pp. 3-24). Dordrecht, Netherlands: Springer.
29. Gilbert, J. K., &, Boulter, C. J., (2000). Developing models in science education. Dordrecht, Netherlands: Kluwer.
30. Hegarty, M., (2004). Dynamic visualizations and learning: Getting to the difficult questions. Learning and Instruction, 14, 343-351.
31. Hegarty, M., (2005). Multimedia learning about physical systems. In, R. E. Mayer, (Ed.), The Cambridge handbook of multimedia learning (pp. 447-465). New York, New York: Cambridge University Press.
32. Hoffler, T. N., &, Leutner, D., (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17, 722-738.
33. Izsak, A., (2004). Students' coordination of knowledge when learning to model physical situations. Cognition and Instruction, 22, 81-128.
34. Johnstone, A. H., (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75-83.
35. Kafai, Y. B., (2006). Constructionism. In, K. Sawyer, (Ed.), Cambridge handbook of the learning sciences (pp. 35-46). New York, New York: Cambridge University Press.
36. Kali, Y., (2006). Collaborative knowledge building using the design principles database. International Journal of Computer-Supported Collaborative Learning, 1, 187-201.
37. Klahr, D., Triona, L. M., &, Williams, C., (2007). Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children. Journal of Research in Science Teaching, 44, 183-203.
38. Kozma, R., (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction, 13, 205-226.
39. 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. Mandle, (Eds.), International perspectives on the design of technology-supported learning environments (pp. 41-60). Mahwah, NJ: Lawrence Erlbaum Associates.
40. Lee, H.-S., Liu, O. L., &, Linn, M. C., (2011). Validating measurement of knowledge integration science using multiple-choice and explanation items. Applied Measurement in Education, 24, 115-136.
41. Lewis, E. L., (1996). Conceptual change among middle school students studying elementary thermodynamics. Journal of Science Education and Technology, 5, 3-31.
42. Linn, M. C., (2005). WISE design for lifelong learning-pivotal cases. In, P. Gärdenfors, &, P. Johansson, (Eds.), Cognition, education and communication technology (pp. 223-256). Mahwah, NJ: Lawrence Erlbaum Associates.
43. Linn, M. C., Chang, H.-Y., Chiu, J., 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 (pp. 239-262). New York, New York: Routledge.
44. Linn, M. C., &, Eylon, B. S., (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York, New York: Routledge.
45. Linn, M. C., &, Hsi, S., (2000). Computers, teachers, and peers: Science learning partners. Mahwah, NJ: Lawrence Erlbaum Associates.
46. Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., &, Chiu, J. L., (2006). Teaching and assessing knowledge integration in science. Science, 313, 1049-1050.
47. Liu, O. L., Lee, H.-S., Hofstetter, C., &, Linn, M. C., (2008). Assessing knowledge integration in science: Construct, measures and evidence. Educational Assessment, 13, 33-55.
48. Liu, O. L., Lee, H.-S., &, Linn, M. C., (2010). Multifaceted assessment of inquiry-based science learning. Educational Assessment, 15, 69-86.
49. Lowe, R., (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 157-176.
50. Lowe, R., (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257-274.
51. Mathison, M. A., (1996). Writing the critique, a text about a text. Written Communication, 13, 314-354.
52. Mayer, R. E., (2009). Multimedia learning (2nd ed.). New York, New York: Cambridge University Press.
53. Mayer, R. E., &, Moreno, R., (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14, 87-99.
54. McElhaney, K. W., &, Linn, M. C., (2011). Investigations of a complex, realistic task: Intentional, unsystematic, and exhaustive experimenters. Journal of Research in Science Teaching, 48, 745-770.
55. Moreno, R., &, Mayer, R. E., (2007). Interactive multimodal learning environments-Special issue on interactive learning environments: Contemporary issues and trends. Educational Psychology Review, 19, 309-326.
56. Moreno, R., &, Valdez, A., (2005). Cognitive load and learning effects of having students organize pictures and words in multimedia environments: The role of student interactivity and feedback. Educational Technology Research and Development, 53, 35-45.
57. National Research Council. (1996). National science education standards. Washington, DC: National Academy.
58. Parnafes, O., (2007). What does "fast" mean? Understanding the physical world through computational representations. Journal of the Learning Sciences, 16, 415-450.
59. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G.,., Soloway, E., (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13, 337-386.
60. Reiser, B. J., (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13, 273-304.
61. Russell, J., &, Kozma, R., (2005). Assessing learning from the use of multimedia chemical visualization software. In, J. Gilbert, (Ed.), Visualization in science education (pp. 299-332). London: Kluwer.
62. Ryoo, K. L., &, Linn, M. C., (2012). Can dynamic visualizations improve middle school students' understanding of energy in photosynthesis ? Journal of Research in Science Teaching, 49, 218-243.
63. 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, 21, 253-279.
64. Schauble, L., Glaser, R., Duschl, R., Schulze, S., &, John, J., (1995). Students' understanding of the objectives and procedures of experimentation in the science classroom. Journal of the Learning Sciences, 4, 131-166.
65. Schwarz, C. V., &, White, B. Y., (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23, 165-205.
66. Smith, G. G., &, Olkun, S., (2005). Why interactivity works: Interactive priming of mental rotation. Journal of Educational Computing Research, 32, 93-111.
67. Tabak, I., (2009, April). Epistemology & learning in the disciplines: Cross-domain epistemological views of science versus humanities students. In M. C. Linn (Chair), Critique to learn science. Symposium conducted at the meeting of the National Association for Research in Science Teaching, Garden Grove, CA.
68. Taylor, R., (2009, April). The influence of students' epistemic beliefs on their judgments of science explanation quality. In M. C. Linn (Chair), Critique to learn science. Symposium conducted at the meeting of the National Association for Research in Science Teaching, Garden Grove, CA.
69. Tiberghien, A., (1985). Heat and temperature: Part B: The development of ideas with teaching. In, R. Driver, E. Guesne, &, A. Tiberghien, (Eds.), Children's ideas in science (pp. 66-84). Milton Keynes, UK: Open University Press.
70. Triona, L. M., &, Klahr, D., (2003). Point and click or grab and heft: Comparing the influence of physical and virtual instructional materials on elementary school students' ability to design experiments. Cognition and Instruction, 21, 149-173.
71. Tversky, B., (2002). Some ways that graphics communicate. In, N. Allen, (Ed.), Working with words and images: New steps in an old dance (pp. 57-74). Westport, CT: Ablex.
72. Tversky, B., Morrison, J. B., &, Betrancourt, M., (2002). Animation: Can it facilitate ? International Journal of Human-Computer Studies, 57, 247-262.
73. Varma, K., (2008). Supporting students' experimentation strategies with dynamic visualizations. Poster presented at the 8th International Conference of the Learning Sciences, International Perspectives in the Learning Sciences: Cre8ting a Learning World, Utrecht, The Netherlands.
74. Veermans, K., van Joolingen, W., &, de Jong, T., (2006). Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. International Journal of Science Education, 28, 341-361.
75. White, B. Y., &, Frederiksen, J. R., (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3-118.
76. White, B. Y., &, Frederiksen, J. R., (2000). Metacognitive facilitation: An approach to making scientific inquiry accessible to all. In, J. Minstrell, &, E. H. van Zee, (Eds.), Inquiring into inquiry learning and teaching in science (pp. 331-370). Washington, DC: AAAS.
77. White, R., &, Gunstone, R., (1992). Probing understanding. New York, New York: The Falmer Press.
78. Williams, M., Debarger, A. H., Montgomery, B. L., Zhou, X., &, Tate, E., (2012). Exploring middle school students' conceptions of the relationship between genetic inheritance and cell division. Science Education, 96, 78-103. doi: 10.1002/sce.20465
79. Wiser, M., &, Amin, T., (2001). " Is heat hot?" Inducing conceptual change by integrating everyday and scientific perspectives on thermal phenomena. Learning and Instruction, 11, 331-355.
80. Wu, H.-K., (2010). Modeling a complex system: Using novice-expert analysis for developing an effective technology-enhanced learning environment. International Journal of Science Education, 32, 195-219.
81. Wu, H.-K., Krajcik, J. S., &, Soloway, E., (2001). Promoting conceptual understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821-842.
82. Xie, Q., &, Tinker, R., (2006). Molecular dynamics simulations of chemical reactions for use in education. Journal of Chemical Education, 83, 77.
83. 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.
84. 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.
85. Zhang, Z. H., &, Linn, M. C., (2011). Can generating representations enhance learning with dynamic visualizations ? Journal of Research in Science Teaching, 48, 1177-1198.