A visual approach to nanotechnology education

International Journal of Engineering Education

Volumn 28, Issue 5, 2012, Pages 1006-1018

  Xie, Charles ^a   Lee, Hee Sun ^b  

^a The Concord Consortium   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Computer simulation; Knowledge integration; Nanoscience; Nanotechnology; Science education; Scientific visualization

Indexed keywords

INSTRUCTIONAL STRATEGY; INTERACTIVE SIMULATIONS; KNOWLEDGE INTEGRATION; NANOTECHNOLOGY EDUCATION; PILOT STUDIES; SCIENCE EDUCATION; SOFTWARE ENVIRONMENTS;

COMPUTER SIMULATION; DATA VISUALIZATION; MATHEMATICAL MODELS; NANOTECHNOLOGY; VIRTUAL REALITY;

NANOSCIENCE;

EID: 84867808399     PISSN: 0949149X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (60)

1. E. Drexler, Engines of Creation 2.0: The Coming Era of Nanotechnology: WOWIO Books, 2007.
2. Battelle Memorial Institute and Foresight Nanotech Institute, http://www.foresight.org/roadmaps/, Accessed 26 April, 2012
3. E. Drexler, http://www.nanowerk.com/spotlight/spotid=15067.php, Accessed 26 April, 2012
4. E. Drexler, Nanosystems: Molecular Machinery, Manufacturing, and Computation, 1st ed.: Wiley, 1992.
5. C. Xie, R. F. Tinker, B. Tinker, A. Pallant, D. Damelin and B. Berenfeld, Computational Experiments for Science Education, Science, 332, 2011, pp. 1516-1517.
6. A. R. Leach, Molecular Modeling: Principles and Applications, 2nd ed.: Pearson Education, 2001.
7. E. Shipley and T. Moher, Instructional Framing for Nanoscale Self-Assembly Design in Middle School: A Pilot Study, in Annual Meeting of the American Educational Research Association, March 24-28, 2008, New York City, 2008.
8. R. P. Feynman, There's Plenty of Room at the Bottom, Journal of Microelectromechanical Systems, 1, 1992, pp. 60-66.
9. R. P. Feynman, R. D. Leighton and M. Sands, The Feynman lectures on physics, Addison-Wesley, Menlo Park, 1963.
10. D. C. Rappaport, The Art of Molecular Dynamics Simulation, Cambridge University Press, Cambridge, UK, 1997.
11. A. Arkhipov, P. L. Freddolino, K. Imada, K. Namba and K. Schulten, Coarse-Grained Molecular Dynamics Simulations of a Rotating Bacterial Flagellum, Biophysical Journal, 91, 2006, pp. 4589-4597.
12. A. Arkhipov, Y. Yin and K. Schulten, Four-Scale Description of Membrane Sculpting by BAR Domains, Biophysical Journal, 95, 2008, pp. 2806-2821.
13. J. G. Gay and B. J. Berne, Modification of the overlap potential to mimic a linear site-site potential, Journal of Chemical Physics, 74, 1981, pp. 3316-3319.
14. D. J. Cleaver, C. M. Care, M. P. Allen and M. P. Neal, Extension and generalization of the Gay-Berne potential, Physical Review E, 54, 1996, pp. 559-567.
15. C. Xie, http://www.concord.org/qxie/papers/beyondlj.pdf, Accessed 26 April, 2012
16. A. Nealen, M. Mü ller, R. Keiser, E. Boxerman and M. Carlson, Physically Based Deformable Models in Computer Graphics, in EUROGRAPHICS Dublin, Ireland, 2005.
17. IBM, http://www.almaden.ibm.com/almaden/media/mirage.html, Accessed 26 April, 2012
18. N. Watanabe and M. Tsukada, Fast and stable method for simulating quantum electron dynamics, Physical Review E, 62, 2000, pp. 2914-2923.
19. L. Lehtovaara, J. Toivanen, and J. Eloranta, Solution of time-independent Schrödinger equation by the imaginary time propagation method, Journal of Computational Physics, 221, 2007, pp. 148-157.
20. Scanning Tunneling Microscopy, http://mw2.concord.org/tmp.jnlp?address= http://mw2.concord.org/public/part1/stm/index.cml (Java is required to open this module), Accessed 26 April, 2012
21. Molecular Self-Assembly, http://mw2.concord.org/tmp.jnlp?address=http:// mw2.concord.org/public/part1/selfassembly/index.cml (Java is required to open this module), Accessed 26 April, 2012
22. Atomic Layer Deposition: A Virtual Experiment, http://mw2.concord.org/ tmp.jnlp?address=http://mw2.concord.org/public/student/nano/alcvd.cml (Java is required to open this module), Accessed 26 April, 2012
23. J. L. Kolodner, Facilitating the Learning of Design Practices: Lessons Learned from an Inquiry into Science Education, Journal of Industrial Teacher Education, 39, 2002.
24. Algoryx Simulation AB, http://www.algodoo.com/wiki/Education, Accessed 26 April, 2012
25. J. Casperson and M. C. Linn, Using visualizations to teach electrostatics, American Journal of Physics, 74, 2006, pp. 316-323.
26. T. J. José and V. M. Williamson, Molecular visualization in science education: An evaluation of the NSF-sponsored workshop, Journal of Chemical Education, 82, 2005, pp. 937-942.
27. J. K. Gilbert, Visualization in Science Education: Springer, 2005.
28. C. E. Wieman, W. K. Adams and K. K. Perkins, PhET: Simulations That Enhance Learning, Science, 322, 2008, pp. 682-683.
29. C. E. Wiemanand K. K. Perkns,Apowerful tool for teaching science, Nature Physics, 2, 2006, pp. 290-292.
30. R. K. Lowe, Interrogation of a dynamic visualisation during learning, Learning and Instruction, 14, 2004, pp. 257-274.
31. M. C. Linn, H.-Y. Chang, J. Chiu, H. Zhang and K. McElhaney, Can Desirable Difficulties Overcome Deceptive Clarity in Scientific Visualizations?, in Successful Remembering and Successful Forgetting: A Festschrift in Honor of Robert A. Bjork, The Taylor & Francis Group, London, England, 2010.
32. M. Alavi and D. Dufner, Technology-mediated collaborative learning: A research perspective, in Learning together online: Research on asynchronous learning netwroks., S. R. Hiltz and R. Goldman, Eds. Lawrence Earlbaum Associates, Mahwah, NJ, 2005.
33. R. Kozma, Students collaborating with computer models and physical experiments, in Proceedings of the Conference on Computer-Supported Collaborative Learning 1999, J. Roschelle and C. Hoadley, Eds. Erlbaum, Mahwah, NJ, 2000, pp. 314-322.
34. J. J. Williams and T. Lombrozo, The Role of Explanation in Discovery and Generalization: Evidence From Category Learning, Cognitive Science: A Multidisciplinary Journal, 34, 2010, pp. 776-806.
35. R. E. Mayer, Should There Be a Three-Strikes Rule Against Pure Discovery Learning? The Case for Guided Methods of Instruction, American Psychologist, 59, 2004, pp. 14-19.
36. S. Papert, 'Situating Constructionism,' in Constructionism, I. Harel and S. Papert, Eds. Ablex Publishing Corporation, Norwood, NJ, 1991.
37. C. Xie and A. Pallant, Constructive Chemistry:ACase Study of Gas Laws, @Concord, 13, 2009, pp. 12-13.
38. A. Greenberg, Integrating nanoscience into the classroom: perspectives on nanoscience education projects, ACS Nano, 3, 2009, pp. 762-769.
39. Quantum Basics, http://mw2.concord.org/tmp.jnlp?address=http://mw2. concord.org/public/part1/etquantum/index.cml (Java is required to open this module), Accessed 26 April, 2012.
40. Quantum Tunneling, http://mw2.concord.org/tmp.jnlp?address=http://mw2. concord.org/public/part1/tunneling/index.cml (Java is required to open this module), Accessed 26 April, 2012.
41. Semiconductors, http://mw2.concord.org/tmp.jnlp?address=http://mw2. concord.org/public/part1/semiconductor/index.cml (Java is required to open this module), Accessed 26 April, 2012.
42. L. Dusenbury, R. Brannigan, M. Falco and W. B. Hansen,A review of research on fidelity of implementation: Implications for drug abuse prevention in school settings, Health Education Research, 18, 2003
43. C. L. O'Donnell, Defining, conceptualizing, and measuring fidelity of implementation and its relationship to outcomes in K-12 curriculum intervention research, Review of Educational Research, 78, 2008, pp. 33-84.
44. W. T. Summerfelt, Program strength and fidelity to evaluation, Applied Developmental Science, 7, 2003, pp. 55-61.
45. H.-S. Lee, O. L. Liu and M. C. Linn, Validating measurement of knowledge integration in science using multiplechoice and explanation items, Applied Measurement in Education, 24, 2011, pp. 115-136.
46. L. O. Liu, H. S. Lee, C. Hofstetter and M. C. Linn, Assessing knowledge integration in science: Construct, measures, and evidence, Educational Assessment, 13, 2008, pp. 1-23.
47. M. Linn, H.-S. Lee, R. Tinker, R. Husic and J. Chiu, Teaching and assessing knowledge integration in science, Science, 313, 2006, pp. 1049-1050.
48. M. Linn, The Knowledge Integration Perspective on Learning and Instruction, in The Cambridge Handbook of the Learning Sciences, R. K. Sawyer, Ed. Cambridge University Press, New York, 2006, pp. 243-264.
49. H.-S. Lee, M. C. Linn, K. Varma and O. L. Liu, How does technology-enhanced inquiry instruction with visualizations impact classroom learning?, Journal of Research in Science Teaching, 47, 2010, pp. 71-90.
50. O. L. Liu, H.-S. Lee and M. C. Linn, Multi-faceted assessment of inquiry-based science learning, Educational Assessment, 15, 2010, pp. 69-96.
51. O. L. Liu, H.-S. Lee and M. C. Linn, Measuring knowledge integration: Validation of four-year assessments, Journal of Research in Science Teaching, 48, 2011, pp. 1079-1107.
52. L. Deslauriers and C. E. Wieman, Learning and retention of quantum concepts with different teaching methods, Physical Review Special Topics-Physics Education Research, 7, 2011, p. 010101.
53. M. Ayene, J. Kriek, and B. Damtie, Wave-particle duality and uncertainty principle: Phenomenotraphic categories of description of tertiary physics students' depictions, Physical Review Special Topics-Physics Education Research, 7, 2011, p. 020113.
54. S. B. McKagan, K. K. Perkins and C. E. Wieman, Design and validation of the quantum mechanics conceptual survey, Physical Review Special Topics-Physics Education Research, 6, 2010, p. 020121.
55. C. Baily and N. D. Finkelstein, Teaching and understanding of quantum interpretations in modern physics courses, Physical Review Special Topics-Physics Education Research, 6, 2010, p. 010101.
56. A. Mason and C. Singh, Do advanced physics students learn from their mistakes without explicit intervention?, American Journal of Physics, 78, 2010, pp. 760-767.
57. S. Embretson and J. Gordin, Improving construct validity with cognitive psychology principles, Journal of Educational Measurement, 38, 2001, pp. 343-368.
58. M. C. Rodriguez, Construct equivalence of multiple-choice and constructed-response items: A random effects synthesis of correlations, Journal of Educational Measurement, 40, 2003, pp. 163-184.
59. T. D. Cook and D. T. Campbell, Quasi-Experimentation: Design & analysis issues for field settings. Houghton Mifflin, Boston, MA, 1979.
60. A. Collins, D. Joseph and K. Bierlaczyc, Design research: Theoretical and methodological issues, Journal of the Learning Sciences, 13, 2004, pp. 43-76.