RESEARCH PAPER
Perceiving the usage of external representations in physics
 
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1
Independent Researcher, Essen, GERMANY
 
2
Institute for Educational Research in the School of Education, University of Wuppertal, Wuppertal, GERMANY
 
3
Didactics of Physics, University of Duisburg-Essen, Essen, GERMANY
 
 
Online publication date: 2023-06-21
 
 
Publication date: 2023-08-01
 
 
EURASIA J. Math., Sci Tech. Ed 2023;19(8):em2311
 
KEYWORDS
ABSTRACT
Prior research shows the importance of external representations in learning physics at school. This research often focuses on the teaching of as well as learning with different forms of representations, such as graphs and tables, and their impact on understanding professional content. Teachers’ and students’ perception and the matching of both have not been in the focus of previous research. One open question in this regard is, how teachers estimate the adequacy of how they use external representations to teach physics compared to how students perceive it. To investigate this question, we conducted a survey of teachers as well as students of 6th, 8th, and 10th grade in German schools. The development and validation of the questionnaire is part of the research method. The results show differences between how teachers estimate the frequency and adequacy of the representations they use and how adequate students perceive this to be. As a practical consequence, these insights could be used for teachers to reflect upon the materials they use to teach physics.
REFERENCES (44)
1.
Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33, 131-152. https://doi.org/10.1016/S0360-....
 
2.
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198. https://doi.org/10.1016/j.lear....
 
3.
Barton, M. L., Heidema, C., & Jordan, D. (2002). Teaching reading in mathematics and science. Educational Leadership, 60, 24-28.
 
4.
Bernholt, S., Härtig, H. & Retelsdorf, J. (2022). Reproduction rather than comprehension? Analysis of gains in students’ science text comprehension. Research in Science Education, 53, 493-506. https://doi.org/10.1007/s11165....
 
5.
Best, R. M., Rowe, M., Ozuru, Y., & McNamara, D. S. (2005). Deep-level comprehension of science texts: The role of the reader and the text. Top Lang Disorders, 25(1), 68-83. https://doi.org/10.1097/000113....
 
6.
Bruner, J. (1967). Toward a theory of instruction. Harvard University Press.
 
7.
Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98(1), 182-197. https://doi.org/10.1037/0022-0....
 
8.
Cromley, J. G., Snyder-Hogan, L. E., & Luciw-Dubas, U. A. (2010a). Cognitive activities in complex science text and diagrams. Contemporary Educational Psychology, 35(1), 59-74. https://doi.org/10.1016/j.cedp....
 
9.
Cromley, J. G., Snyder-Hogan, L. E., & Luciw-Dubas, U. A. (2010b). Reading comprehension of scientific text: A domain-specific test of the direct and inferential mediation model of reading comprehension. Journal of Educational Psychology, 102(3), 687-700. https://doi.org/10.1037/a00194....
 
10.
Dickmann, T., Opfermann, M., Dammann, E., Lang, M., & Rumann, S. (2019). What you see is what you learn? The role of visual model comprehension for academic success in chemistry. Chemistry Education Research and Practice, 20, 804-820.
 
11.
DiSessa, A. A. (2004). Meta-representation: Native competence and targets for instruction. Cognition and Instruction, 22, 293-331. https://doi.org/10.1207/s15326....
 
12.
Ditton, H. (2002). Lehrkräfte und Unterricht aus Schülersicht: Ergebnisse einer Untersuchung im Fach Mathematik [Teachers and teaching from the students’ point of view: Results of an investigation in the subject mathematics]. Zeitschrift Für Pädagogik [Journal of Pedagogy], 48(2), 262-286.
 
13.
Edwards, A. L. (1957). The social desirability variable in personality assessment and research. Dryden Press.
 
14.
Fang, Z., & Wei, Y. (2010). Improving middle school students’ science literacy through reading infusion. The Journal of Educational Research, 103(4), 262-273. https://doi.org/10.1080/002206....
 
15.
Field, A. (2013). Discovering statistics using IBM SPSS Statistics: And sex and drugs and Rock “N” Roll (4th Edn.). Sage.
 
16.
Fredlund, T., Airey, J., & Linder, C. (2012). Exploring the role of physics representations: An illustrative example from students sharing knowledge about refraction. European Journal of Physics, 33(3), 657-666. https://doi.org/10.1088/0143-0....
 
17.
Gilabert, R., Martínez, G., & Vidal-Abarca, E. (2005). Some good texts are always better: Text revision to foster inferences of readers with high and low prior background knowledge. Learning and Instruction, 15(1), 45-68. https://doi.org/10.1016/j.lear....
 
18.
Helmke, A., & Schrader, F.‑W. (2019). Qualitätsmerkmale “guten Unterrichts” [Quality criteria for ‘good teaching’]. In C. Hof, T. Fuhr, W. Wittenbruch, S. Hellekamps, W. Plöger, & P. Gonon (Eds.), Handbuch der Erziehungswissenschaft [Educational science handbook] (pp. 701-712). Schöningh.
 
19.
Horz, H., & Schnotz, W. (2010). Cognitive load in learning with multiple representations. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 229-252). Cambridge University Press. https://doi.org/10.1017/CBO978....
 
20.
Kalyuga, S. (2014). The expertise reversal principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 576-597). Cambridge University Press. https://doi.org/10.1017/CBO978....
 
21.
Kämpfe, N. (2009). Schülerinnen und Schüler als Experten für Unterricht [Pupils as experts for teaching]. Zeitschrift Für Erziehungswissenschaft, Bildungspolitik Und Pädagogische Praxis [Journal for Educational Science, Educational Policy and Educational Practice], 101(2), 149-163.
 
22.
Könings, K. D., Seidel, T., Brand-Gruwel, S., & van Merriënboer, J. J. G. (2014). Differences between students’ and teachers’ perceptions of education: Profiles to describe congruence and friction. Instructional Science, 42(1), 11-30. https://doi.org/10.1007/s11251....
 
23.
Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Models and modeling in science education: Visualization in science education (pp. 121-145). Springer. https://doi.org/10.1007/1-4020....
 
24.
Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65-99. https://doi.org/10.1111/j.1551....
 
25.
Leisen, W., Opfermann, M., & Härtig, H. (2021). Wahrnehmung von Repräsentationen im Physikunterricht [Perception of representations in physics lessons] In S. Habig (Ed.), Naturwissenschaftlicher Unterricht und Lehrerbildung im Umbruch? [Science and teacher education in upheaval?] (pp. 302-305). University of Duisburg-Essen.
 
26.
Mayer, R. E. (2020). Multimedia learning. Cambridge University Press. https://doi.org/10.1017/978131....
 
27.
Mayer, R. E., & Fiorella, L. (2014). Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 279-315). Cambridge University Press. https://doi.org/10.1017/CBO978....
 
28.
Nitz, S., Nerdel, C., & Prechtl, H. (2012). Entwicklung eines Erhebungsinstruments zur Erfassung der Verwendung von Fachsprache im Biologieunterricht [Development of an instrument to assess the usage of technical language in biology lessons]. Zeitschrift Für Didaktik Der Naturwissenschaften [Journal of Science Education], 18, 117-135.
 
29.
Opfermann, M., Schmeck, A., & Fischer, H. (2017). Multiple representations in physics and science education – Why should we use them? In D. Treagust, R. Duit & H. Fischer (Eds.), Multiple representations in physics education (pp. 1-22). Springer.
 
30.
O’Reilly, T., & McNamara, D. S. (2007). Reversing the reverse cohesion effect: Good texts can be better for strategic, high-knowledge readers. Discourse Processes, 43(2), 121-152. https://doi.org/10.1080/016385....
 
31.
Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.
 
32.
Pineker-Fischer, A. (2017). Sprach-und Fachlernen im naturwissenschaftlichen Unterricht [Language and subject learning in science lessons]. Springer. https://doi.org/10.1007/978-3-....
 
33.
Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30-46. https://doi.org/10.1037/a00372....
 
34.
Sanchez, C. A., & Wiley, J. (2006). An examination of the seductive details effect in terms of working memory capacity. Memory & Cognition, 34(2), 344-355. https://doi.org/10.3758/BF0319....
 
35.
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 University Press. https://doi.org/10.1017/CBO978....
 
36.
Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141-156. https://doi.org/10.1016/S0959-....
 
37.
Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13, 227-237. https://doi.org/10.1016/S0959-....
 
38.
Spiro, R. J., & Jehng, J.‑C. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multidimensional transversal of complex subject matter. In D. Nix, & R. J. Spiro (Eds.), Cognition, education and multimedia: Exploring ideas in high technology (pp. 163-205). Lawrence Erlbaum.
 
39.
Stender, A., Schwichow, M., Zimmerman, C., & Härtig, H. (2018). Making inquiry-based science learning visible: The influence of CVS and cognitive skills on content knowledge learning in guided inquiry. International Journal of Science Education, 15, 1812-1831. https://doi.org/10.1080/095006....
 
40.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-....
 
41.
Treagust, D. F., Duit, R., & Fischer, H. E. (Eds.) (2017). Models and modeling in science education: Multiple representations in physics education. Springer. https://doi.org/10.1007/978-3-....
 
42.
van der Meij, J., & de Jong, T. (2006). Progression in multiple representations: Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16, 199-212. https://doi.org/10.1016/j.lear....
 
43.
van Oostendorp, H., & Goldman, S. R. (Eds.) (2009). The construction of mental representations during reading. Lawrence Erlbaum.
 
44.
Waldrip, B., & Prain, V. (2012). Learning from and through representations in science. In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 145-155). Springer. https://doi.org/10.1007/978-1-....
 
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