Understanding Students’ Free-Body Diagrams Using the Metarepresentations Survey for Physics





metarepresentations, free body diagrams, construct validity, structural equation modeling, Rasch modeling


Study purpose. The Metarepresentations Survey for Physics (MSP) was developed to assess students’ metarepresentational knowledge during physics problem solving. 

Materials and methods. The survey was given to 288 introductory-level college physics students. Psychometric properties of the instrument, including construct validity, were evaluated by confirmatory factor analysis and Rasch analysis. 

Results. We also examined students’ beliefs about the use of free-body diagrams, as well as thoroughly examined the link between students’ problem solving success and free-body diagrams. 

Conclusions. We recommend the use of the MSP for physics instructors and science education researchers who want to evaluate students’ free-body diagrams. Additionally, we suggest the subject of physics can be replaced with chemistry, genetics, or another science to assess metarepresentations in other domains. 

Author Biographies

Gita Taasoobshirazi, Kennesaw State University

School of Data Science and Analytics, 1000 Chastain Road, Kennesaw GA 30144; 404-426-4483, USA.

Benjamin C. Heddy, University of Oklahoma

Learning Sciences Program, College of Education, 820 Van Vleet Oval, Norman OK 73019; 405-325-5974, USA

Robert W. Danielson, Washington State University

College of Education, 412 E. Spokane Falls Blvd., Spokane WA 99210; 509-358-7793, USA

Eric R.I. Abraham, University of Oklahoma

Department of Physics and Astronomy, 440 W. Brooks St., Norman OK 73019; 405-325-6481, USA

Shelby Joji, Kennesaw State University

1000 Chastain Road, Kennesaw GA 30144, USA


Supeno, S., Subiki, S., & Rohma, L. W. (2018). Students’ Ability in Solving Physics Problems on Newtons’ Law of Motion. http://repository.unej.ac.id/handle/123456789/92723 DOI: https://doi.org/10.24042/jipfalbiruni.v7i1.2247

Kohl, P. B., & Finkelstein, N. D. (2006). Effects of representation on students solving physics problems: A fine-grained characterization. Physical review special topics-Physics education research, 2(1), 010106. https://doi.org/10.1103/PhysRevSTPER.2.010106 DOI: https://doi.org/10.1103/PhysRevSTPER.2.010106

diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and instruction, 22(3), 293-331. https://doi.org/10.1207/s1532690xci2203_2 DOI: https://doi.org/10.1207/s1532690xci2203_2

Rosengrant, D., Van Heuvelen, A., & Etkina, E. (2009). Do students use and understand free-body diagrams? Physical Review Special Topics-Physics Education Research, 5(1), 010108. https://doi.org/10.1103/PhysRevSTPER.5.010108 DOI: https://doi.org/10.1103/PhysRevSTPER.5.010108

Taasoobshirazi, G., & Carr, M. (2009). A structural equation model of expertise in college physics. Journal of Educational Psychology, 101(3), 630. https://doi.org/10.1037/a0014557 DOI: https://doi.org/10.1037/a0014557

Van Heuvelen, A., & Zou, X. (2001). Multiple representations of work–energy processes. American Journal of Physics, 69(2), 184-194. https://doi.org/10.1119/1.1286662 DOI: https://doi.org/10.1119/1.1286662

Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385-398. https://doi.org/10.1016/S0732-3123(01)00051-7 DOI: https://doi.org/10.1016/S0732-3123(01)00051-7

Taasoobshirazi, G., Bailey, M., & Farley, J. (2015). Physics metacognition inventory part II: confirmatory factor analysis and rasch analysis. International Journal of Science Education, 37(17), 2769-2786. https://doi.org/10.1080/09500693.2015.1104425 DOI: https://doi.org/10.1080/09500693.2015.1104425

Flavell, J. H. (1985). Cognitive development (Second Edition). Englewood Cliffs, NJ: Prentice Hall.

Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20(4), 391-409. https://doi.org/10.1007/s10648-008-9083-6 DOI: https://doi.org/10.1007/s10648-008-9083-6

Schraw, G. (2001). Promoting general metacognitive awareness. In Metacognition in learning and instruction (pp. 3-16). Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2243-8_1 DOI: https://doi.org/10.1007/978-94-017-2243-8_1

Veenman, M. V. (2007). The assessment and instruction of self-regulation in computer-based environments: a discussion. Metacognition and Learning, 2(2-3), 177-183. https://doi.org/10.1007/s11409-007-9017-6 DOI: https://doi.org/10.1007/s11409-007-9017-6

Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in science education, 36(1-2), 111-139. https://doi.org/10.1007/s11165-005-3917-8 DOI: https://doi.org/10.1007/s11165-005-3917-8

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475. https://doi.org/10.1006/ceps.1994.1033 DOI: https://doi.org/10.1006/ceps.1994.1033

Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. sage. DOI: https://doi.org/10.4135/9781412984898

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit In Bollen KA, Long JS, editors. Testing structural equation models. Beverly Hills, CA: Sage, 111-135.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118

Kline, R. B. (2016). Methodology in the social sciences. Principles and practice of structural equation modeling (4th ed.): Guildford Press.

Lim, S. M., Rodger, S., & Brown, T. (2009). Using Rasch analysis to establish the construct validity of rehabilitation assessment tools. International Journal of Therapy & Rehabilitation, 16(5), 251–260. https://doi.org/10.12968/ijtr.2009.16.5.42102 DOI: https://doi.org/10.12968/ijtr.2009.16.5.42102

Boone, W. J., Townsend, J. S., & Staver, J. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: An exemplar utilizing STEBI self‐efficacy data. Science Education, 95(2), 258-280. https://doi.org/10.1002/sce.20413 DOI: https://doi.org/10.1002/sce.20413

Baghaei, P. (2008). The Rasch model as a construct validation tool. Rasch Measurement Transactions, 22(1), 1145-1146.

Linacre, J. M. (2012). A user’s guide to WINSTEPS MINISTEP. Rasch model computer programs. Beaverton, Oregon: Winsteps. com.

Wright, B. D., & Stone, M. H. (1979). Best test design.

Boone, W. J., Staver, J. R., & Yale, M. S. (2013). Rasch analysis in the human sciences. Springer Science & Business Media. DOI: https://doi.org/10.1007/978-94-007-6857-4

Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Psychology Press.

Priest, A. G., & Lindsay, R. O. (1992). New light on novice—expert differences in physics problem-solving. British Journal of Psychology, 83(3), 389-405. https://doi.org/10.1111/j.2044-8295.1992.tb02449.x DOI: https://doi.org/10.1111/j.2044-8295.1992.tb02449.x

Wayne, T. (2020). Free body diagrams: the basics. http://www.mrwaynesclass.com/freebodies/reading/index01.html




How to Cite

Taasoobshirazi, G., Heddy, B. C., Danielson, R. W., Abraham, E. R., & Joji, S. (2022). Understanding Students’ Free-Body Diagrams Using the Metarepresentations Survey for Physics. Journal of Learning Theory and Methodology, 3(3), 93–101. https://doi.org/10.17309/jltm.2022.3.01



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