Strongly and weakly directed approaches to teaching multiple representation use in physics

(Some reference links may require a separate subscription.)

1. J. H. Larkin, in Mental Models, edited by D. Gentner and A. Stevens (Lawrence Erbaum, Mahwah, NJ, 1983).
2. F. Reif, Millikan Lecture 1994: Understanding and teaching important scientific thought processes, Am. J. Phys. 63, 17 (1995) [SPIN].
3. A. Van Heuvelen, Learning to think like a physicist: A review of research-based instructional strategies, Am. J. Phys. 59, 891 (1991) [SPIN][INSPEC].
4. A. Van Heuvelen and X. Zou, Multiple representations of work-energy processes, Am. J. Phys. 69, 184 (2001) [SPIN][INSPEC].
5. R. J. Dufresne, W. J. Gerace, and W. J. Leonard, Solving physics problems with multiple representations, Phys. Teach. 35, 270 (1997) [SPIN].
6. D. P. Maloney, in Handbook of Research on Science Teaching and Learning, edited by D. Gabel (MacMillan, New York 1994).
7. M. Chi, R. Glaser, and E. Rees, in Advances in the Psychology of Human Intelligence, edited by R. J. Sternberg (Lawrence Erlbaum, Hillsdale, NJ, 1982), Vol. 1.
8. R. B. Kozma and J. Russell, Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena, J. Res. Sci. Teach. 34, 949 (1997).
9. R. B. Kozma, The material features of multiple representations and their cognitive and social affordances for science understanding, Learn. Instr. 13, 205 (2003).
10. L. McDermott, in Toward a Scientific Practice of Science Education, edited by M. Gardner, J. Greeno, F. Reif, A. Schoenfeld, A. diSessa, and E. Stage (IOS, Amsterdam 2004).
11. D. Hestenes, M. Wells, and G. Swackhamer, Force concept inventory, Phys. Teach. 30, 141 (1992) [SPIN].
12. R. K. Thornton and D. R. Sokoloff, Assessing student learning of Newton’s laws: The Force and Motion Conceptual Evaluation and the evaluation of Active Learning Laboratory and Lecture curricula, Am. J. Phys. 66, 338 (1998) [SPIN][INSPEC].
13. L. Ding, R. Chabay, B. Sherwood, and R. Beichner, Evaluating an electricity and magnetism assessment tool: Brief electricity and magnetism assessment, Phys. Rev. ST Phys. Educ. Res. 2, 010105 (2006).
14. P. Heller, R. Keith, and S. Anderson, Teaching problem solving through cooperative grouping, parts i and ii, Am. J. Phys. 60, 627 (1992) [SPIN][INSPEC].
15. D. Rosengrant, E. Etkina, and A. Van Heuvelen, Ph.D. thesis, Rutgers, the State University of New Jersey, 2007.
16. C. DeLeone and E. Gire, in Proceedings of the 2005 PERC (AIP, Melville, NY, 2006).
17. D. Rosengrant, E. Etkina, and A. Van Heuvelen, in Proceedings of the 2006 PERC (AIP, Melville, NY, 2007).
18. P. B. Kohl and N. D. Finkelstein, The effects of representation on students solving physics problems: A fine-grained characterization, Phys. Rev. ST Phys. Educ. Res. 2, 010106 (2006).
19. J. P. Mestre, T. C. Thaden-Koch, R. J. Dufresne, and W. J. Gerace, in Research on Physics Education, Proceedings of the International School of Physics “Enrico Fermi” Course CLVI (IOS, Amsterdam, 2004).
20. A. Elby, What students’ learning of representations tells us about constructivism, J. Math. Behav. 19, 481 (2000).
21. E. Etkina, A. Van Heuvelen, S. White-Brahmia, D. T. Brookes, M. Gentile, S. Murthy, D. Rosengrant, and A. Warren, Scientific abilities and their assessment, Phys. Rev. ST Phys. Educ. Res. 2, 020103 (2006).
22. E. Etkina and A. Van Heuvelen, in Proceedings of the 2001 PERC (AIP, Melville, NY, 2002), p. 17.
23. A. Van Heuvelen and E. Etkina, Active Learning Guide (Addison Wesley, San Francisco, 2006).
24. E. Etkina and S. Murthy, in Proceedings of the 2005 PERC (AIP, Melville, NY, 2006).
25. http://www.physics.ohio-state.edu/~physedu/tools.
26. http://www.colorado.edu/physics/EducationIssues/.
27. http://phet.colorado.edu.
28. P. B. Kohl and N. D. Finkelstein, The effect of instructional environment on physics students’ representational skills, Phys. Rev. ST Phys. Educ. Res. 2, 010102 (2006).
29. D. Sheshkin, Handbook of Parametric and Nonparametric Statistical Procedures, 3rd ed. (CRC Press, Boca Raton, 2004).
30. We emphasize that this is an evaluation of the representations contained within the problem, not necessarily an evaluation of all the representations used by the students. Students were intended to (and often did) use FBDs and pictures in many of their solutions of Rutgers exam problems that were strictly mathematical in presentation.
31. We had initially planned to give problem 4 separately, but changed this partway through the study, resulting in some recitation sections not receiving problem 4.
32. We have found that leaving the data unnormalized tends to prompt the conclusion that the Rutgers students substantially outperformed the CU students, neglecting the previously mentioned problem format difference.
33. We will not attempt to argue causation here. It could well be that using multiple representations leads to better performance directly. Alternatively, it could be that better students overall are more likely to use multiple representations. Or, most likely (in our opinions), these are both true, and are intertwined.