A comparison between preservice science teachers’ representational competence and fluency in chemistry and physics
DOI:
https://doi.org/10.36681/tused.2025.015Keywords:
Preservice science teachers, multiple representations, representational competence, descriptive comparative designAbstract
This study examined the representational competence and fluency of preservice science teachers (PSTs) enrolled in a science teacher education program. It compared how these skills influence the understanding of the same cohort of PSTs when teaching concepts in chemistry and physics. Utilising a quantitative descriptive comparative design, the research analyses the participants' ability to effectively use multiple representations (MRs)—comprising graphical, experimental, symbolic, and verbal modes—during lesson presentations. Data from 39 PSTs were collected through video recordings that demonstrate concepts using various representation modes in chemistry and physics. Chi-square statistical analyses revealed significant differences in PSTs' graphical and experimental competence, with no significant differences observed in symbolic and verbal representations. The findings underscore the need for improved pedagogical strategies to enhance representational skills in science education, emphasising the interconnectedness of representational competence and fluency. It calls for targeted approaches in teacher education programs to better equip future educators with the necessary skills to foster effective learning outcomes in science classrooms.
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Abdurrahman, A., Setyamımgsıh, C. A., & Jalmo, T. (2019). Implementing multiple representation- based worksheet to develop critical thinking skills. Journal of Turkish Science Education, 16(1), 138-162.
Alfianti, A., & Kuswanto, H. (2024). Learning analytics approach to improve multiple representation Skills in direct-current circuits. Jurnal Ilmiah Pendidikan Fisika Al-Biruni, 13(1), 23-32.
Ceuppens, S., Deprez, J., Dehaene, W., & Cock, M. D. (2018). Design and validation of a test for representational fluency of 9th grade students in physics and mathematics: The case of linear functions. Physical Review Physics Education Research, 14(2), 1-19. https://doi.org/10.1103/physrevphyseducres.14.020105
Cheng, M. & Gilbert, J. (2009). Towards a better utilization of diagrams in research into the use of representative levels in chemical education. In J.K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education: models and modeling in science education (pp. 55-73). Springer.
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
Dalland, C. P., Klette, K., & Svenkerud, S. (2020). Video studies and the challenge of selecting time scales. International Journal of Research & Method in Education, 43(1), 53-66. https://doi.org/10.1080/1743727X.2018.1563062
Daniel, K. L., Bucklin, C. J., Leone, E. A., & Idema, J. (2018). Towards a definition of representational Competence. In K.L. Daniel (Ed.), Towards a framework for representational competence in science education (pp. 3-11). Springer.
Erniwati, E., Sukariasih, L., Hunaidah, H., Sahara, L., Hasrida, H., Sirih, M., & Fayanto, S. (2020). Analysis of difficulty of science learning-based multi-representation. Jurnal Pendidikan Fisika, 8(3), 263-278.
Farida, I., Liliasari, L., Widyantoro, D. H., & Sopandi, W. (2009). The importance of development of representational competence in chemical problem solving using interactive multimedia. In Proceeding of The Third International Seminar on Science Education (pp. 259-277). UPI.
Follmer, D. J., & Sperling, R. A. (2020). The roles of executive functions in learning from multiple representations and perspectives. In P. Van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Handbook of learning from multiple representations and perspectives (pp. 297–313). Routledge. https://doi.org/10.4324/9780429443961-21
Gilbert, J.K., & Treagust, D.F. (2009). Towards a coherent model for macro, submicro and symbolic representations in chemical education. In J.K. Gilbert & D.F.Treagust (Eds.), Multiple representations in chemical education (pp.333–350). Springer. https://doi.org/10.1007/978-1-4020-8872-8_1
Gkitzia, V., Salta, K., & Tzougraki, C. (2020). Students’ competence in translating between different types of chemical representations. Chemistry Education Research and Practice, 21(1), 307-330. https://doi.org/10.1039/c8rp00301g
Hahn, L., & Klein, P. (2023). The impact of multiple representations on students' understanding of vector field concepts: Implementation of simulations and sketching activities into lecture-based recitations in undergraduate physics. Frontiers in Psychology, 13(2), 1-8. https://doi.org/10.3389/fpsyg.2022.1012787
Halverson, K. L., & Friedrichsen, P. (2013). Learning tree thinking: Developing a new framework of representational competence. In D. F. Treagust & C.-Y. Tsui (Eds.), Models and modeling in science education, multiple representations in biological education (pp. 185–201). Springer.
Handayani, W., & Masrifah, M. (2021). Development physics representational fluency instrument test of electrostatic concept. Journal of Physics: Conference Series, 2098(1), 1-5. https://doi.org/10.1088/1742-6596/2098/1/012009
Handayani, W. (2024). Profile of preservice physics teachers ' representational fluency in the electrostatic concept. KnE Social Sciences, 9(13), 924-931. https://doi.org/10.18502/kss.v9i13.16018
Hansen, J., & Richland, L. E. (2020). Teaching and learning science through multiple representations: Intuitions and executive functions. CBE Life Sciences Education, 19(4), 1-15. https://doi.org/10.1187/cbe.19-11-0253
Heale, R., & Twycross, A. (2015). Validity and reliability in qualitative studies. Evidence-Based Nursing, 18(3), 66-67. https://doi.org/10.1136/eb-2015-102129
Hill, M., & Sharma, M. D. (2015). Students’ representational fluency at university: A cross-sectional measure of how multiple representations are used by physics students using the representational fluency survey. Eurasia Journal of Mathematics, Science and Technology Education, 11(6), 1633-1655. https://doi.org/10.12973/eurasia.2015.1427a
Hilton, A., & Nichols, K. (2011). Representational classroom practices that contribute to students’ Conceptual and representational understanding of chemical bonding. International Journal of Science Education, 33(16), 2215-2246. https://doi.org/10.1080/09500693.2010.543438
Jannah, M, Nasir, M., Siahaan, D. & Soewarno, S. (2022). Analysis of students' difficulties in solving Physics problems with multiple representation using what's another way method. Al-Ishlah: Jurnal Pendidikan, 14(2), 2479-2488.
Kohl, P. B., & Finkelstein, N. (2017). Understanding and promoting effective use of representations in Physics learning. In D. F. Treagust, R. Duit, & H. E. Fischer (Eds.), Multiple representations in physics education (pp. 231-254). Springer. https://doi.org/10.1007/978-3-319-58914-5_11
Kozma, R. B., & Russell, J. (2005). Modelling students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 121–145). Springer.
Kozma, R. B. (2020). Use of multiple representations by experts and novices. In P. Van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Handbook of learning from multiple representations and perspectives (pp. 33-47). Routledge. https://doi.org/10.4324/9780429443961-4
Küchemann, S., Malone, S., Edelsbrunner, P., Lichtenberger, A., Stern, E., Schumacher, R., Brünken, R., Vaterlaus, A., & Kuhn, J. (2021). Inventory for the assessment of representational competence of vector fields. Physical Review Physics Education Research, 17(2), 1-25. https://doi.org/10.1103/physrevphyseducres.17.020126
Lesh, R.A., & Doerr, H. (2003). Foundations of a models and modelling perspective on mathematics teaching, learning, and problem solving. In H. Doerr & R.A. Lesh (Eds.), Beyond constructivism: Models and modelling perspectives on mathematics teaching, learning, and problem solving (pp.3-33). Erlbaum.
Lesh, R., Post, T., & Behr, M. (1987). Representations and translations among representations in mathematics learning and problem solving. In C. Janvier (Ed.), Problems of representations in the teaching and learning of mathematics (pp. 33-40). Erlbaum.
List, A., Meter, P. V., Kendeou, P., & Lombardi, D. (2020). Loggers and conservationists: Navigating the multiple resource forest through the trees. In P. V. Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Handbook of learning from multiple representations and perspectives (pp. 1-13). Routledge. https://doi.org/10.4324/9780429443961-1
Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive analysis in education: A guide for researchers. National Center for Education Evaluation and Regional Assistance.
Lodico, M. G., Spaulding, D. T., & Voegtle, K. H. (2010). Methods in educational research: from theory to practice. John Wiley & Sons.
McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23(2), 143-149. https://doi.org/10.11613/BM.2013.018
Mishra, C., Clase, K.L., Bucklin, C.J., & Daniel, K.L. (2018). Improving students’ representational competence through course-based undergraduate research experience. In K. Daniel (Ed.), Towards a framework for representational competence in science education (pp. 177–201). Springer. https://doi.org/10.1007/978-3-319-89945-9
Moore, T. J., Miller, R. L., Lesh, R., Stohlmann, M., & Kim, Y. R. (2013). Modeling in Engineering: The role of representational fluency in students’ conceptual understanding. Journal of Engineering Education, 102(1), 141–178. https://doi.org/10.1002/jee.20004
Moore, T. J., Selcen Guzey, S., Roehrig, G. H., & Lesh, R. A. (2018). Representational fluency: A means for students to develop STEM literacy. In K. L. Daniel (Ed.), Towards a framework for representational competence in science education (pp. 13–30). Springer. https://doi.org/10.1007/978-3-319-89945-9_2
Munfaridah, N., Avraamidou, L., & Goedhart, M. 2021. The use of multiple representations in undergraduate physics education: what do we know and where do we go from here? Eurasia Journal of Mathematics, Science and Technology Education, 17(1), 1-19. https://doi.org/10.29333/ejmste/9577
Murni, H. P., Azhar, M., Ellizar, E., Nizar, U. K., & Guspatni, G. (2022). Three levels of chemical representation-integrated and structured inquiry-based reaction rate module: Its effect on students' mental models. Journal of Turkish Science Education, 19(3), 758-772.
Nichols, K., Stevenson, M., Hedberg, J., & Gillies, R. (2016). Primary teachers’ representational practices: from competency to fluency. Cambridge Journal of Education, 46(4), 509-531. https://doi.org/10.1080/0305764X.2015.1068741
Nieminen, P., Savinainen, A., & Viiri, J. (2017). Learning about forces using multiple representations. In D. F. Treagust, R. Duit, & H. E. Fischer (Eds.), Multiple representations in physics education: Models and modeling in science education (pp. 163-182). Springer. https://doi.org/10.1007/978-3-319-58914-5_8
Opfermann, M., Schmeck, A., & Fischer, H. E. (2017). Multiple representations in physics and science education–Why should we use them? In D. F. Treagust, R. Duit, & H. E. Fischer (Eds.), Multiple representations in physics education: Models and modeling in science education (pp. 1-22). Springer. https://doi.org/10.1007/978-3-319-58914-5_1
Pande, P., & Chandrasekharan, S. (2021). Expertise as sensorimotor tuning: Perceptual navigation patterns mark representational competence in science. Research in Science Education, 52(2), 725-747. https://doi.org/10.1007/s11165-020-09981-3
Parsons, P. (2018). Promoting representational fluency for cognitive bias mitigation in information visualization. In Geoffrey Ellis (Ed.), Cognitive biases in visualizations (pp. 137-147). Springer. https://doi.org/10.1007/978-3-319-95831-6_10
Popova, M., & Jones, T. (2021). Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills. Chemistry Education Research and Practice, 22(3), 733-748. https://doi.org/10.1039/d0rp00329h
Prain, V., & Tytler, V. (2013). Representing and learning in science. In R. Tytler, V. Prain, P. Hubber, & B. Waldrip (Eds.), Constructing representations to learn in science (pp. 1-14). Springer.
Privitera, G. J., & Ahlgrim-Delzell, L. (2019). Research methods for education. Sage Publications.
Rau, M.A. (2020). Cognitive and socio-cultural theories on competencies and practices involved in learning with multiple external representations. In P. Van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Handbook of learning from multiple representations and perspectives (pp. 17-32). Routledge. https://doi.org/10.4324/9780429443961-3
Siedlecki, S. L. (2020). Understanding descriptive research designs and methods. Clinical Nurse Specialist, 34(1), 8-12. https://doi.org/10.1097/nur.0000000000000493
Sun, J., & Van Es, E. A. (2015). An exploratory study of the influence that analyzing teaching has on preservice teachers’ classroom practice. Journal of teacher education, 66(3), 201-214. https://doi.org/10.1177/0022487115574103
Taber, K. (2009). Learning at the symbolic level. In J.K. Gilbert and D.F. Treagust (Eds.), Multiple representations in chemical education: models and modeling in science education (pp. 75-108). Springer.
Tang, K. S., Delgado, C., & Moje, E. B. (2014). An integrative framework for the analysis of multiple and multimodal representations for meaning‐making in science education. Science Education, 98(2), 305-326.
Tang, Z., Liu, X., Chen, Y., & Yang, B. (2019). The role of multiple representations and representational fluency in cryptography education. Proceedings of the 20th Annual SIG Conference on Information Technology Education, 72, 75-80. http://dx.doi.org/10.1145/3349266.3351412
Tavakol, M., & Sandars, J. (2014) Quantitative and qualitative methods in medical education research. Medical Teacher, 36(9), 746-756. https://doi.org/10.3109/0142159X.2014.915298
Tonyali, B., Ropohl, M. & Schwanewedel, J. (2023). What makes representations good representations for science education? A teacher-oriented summary of significant findings and a practical guideline for the transfer into teaching. Chemistry Teacher International, 5(4), 413-425. https://doi.org/10.1515/cti-2022-0019
Treagust, D., Won, M., & McLure, F. (2018). Multiple representations and students’ conceptual change in science. In T. G. Amin & O. Levrini (Eds.), Converging Perspectives on Conceptual Change (pp. 121-128). Routledge.
Volkwyn, T. S., Airey, J., Gregorcic, B., & Linder, C. (2020). Developing representational competence: linking real-world motion to physics concepts through graphs. Learning: Research and Practice, 6(1), 88-107. https://doi.org/10.1080/23735082.2020.1750670
Widodo, W., Sunarti, T., Setyarsih, W., Jauhariyah, M. N. R., & Zainuddin, A. (2023). The relationship between multiple representational skills and understanding of physics concepts in preservice science teachers. Journal of Physics: Conference Series, 2623(1), 1-10.
Yaman, F., & Hand, B. (2022). Examining pre-service science teachers’ development and utilization of written and oral argument and representation resources in an argument-based inquiry environment. Chemistry Education Research and Practice, 23(4), 948-968. https://doi.org/10.1039/d2rp00152g
Zuhri, R. S., Wilujeng, I., & Haryanto, H. (2023). Multiple representation approach in elementary school science learning: A systematic literature review. International Journal of Learning, Teaching and Educational Research, 22(3), 51-73. https://doi.org/10.26803/ijlter.22.3.4
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