Students’ mental models of solid elasticity: Mixed method study

John Rafafy  Batlolona; Markus  Dıantoro; Wartono; Marleny  Leasa

doi:10.36681/

Authors

John Rafafy Batlolona
Markus Dıantoro
Wartono
Marleny Leasa

DOI:

https://doi.org/10.36681/

Keywords:

Mental models, Problem-based learning, Physics learning, Solid elasticity

Abstract

A mental model (MM) is an internal representation of students’ conceptual understanding. Currently, students have still had difficulties in explaining the physical state of elasticity of solid materials, at sub-microscopic level. These difficulties call for this research. Through a mixed method, the study aimed to reveal the development and differences of students’ mental models after physics learning with problem -based learning (PBL) and conventional methods. Indicators of students’ mental models were adapted from SMD model. Findings suggested that the PBL resulted in more MM, whilst conventional classes emerged MM on the elastic and plastic objects. Meanwhile, the lowest MM achievements ware Hooke’s Law for the PBL, and series and parallel springs for the conventional class. N-Gain values of the students’ mental models at PBL and conventional classes were found to be 0.64 and 0.43 respectively. On the other hand, mental model scores of the PBL learning model was higher (23.77%) than those of the conventional learning model. Thus, it can be concluded that the PBL learning model is effective in improving the students’ mental models of physics. This research recommends that students’ understanding of physics concepts should be increased at macroscopic and sub-microscopic levels.

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References

Akaygun, S. (2016). Is the oxygen atom static or dynamic? the effect of generating animations on students’ mental models of atomic structure. Chemistry Education Research and Practice, 17(4), 788–807. https://doi.org/10.1039/c6rp00067c.

Akçay, B. (2009). Problem-based learning in science education. Journal of Turkish Science Education, 6(1), 26–36.

Arends, R I. (2012). Learning to Teach ninth edition. New York : McGraw-Hill.

Altan Kurnaz, M., & Eksi, C. (2015). An analysis of high school students’ mental models of solid friction in physics. Educational Sciences: Theory & Practice, 15(3), 787–795. https://doi.org/10.12738/estp.2015.3.2526.

Askell‐Williams, H., Murray‐Harvey, R., & Lawson, M. J. (2007). Teacher education students’ reflections on how problem‐based learning has changed their mental models about teaching and learning. Teacher Educator, 42(4), 237–263. https://doi.org/10.1080/08878730709555406.

Batlolona, J. R. (2017). Model mental dan keterampilan berpikir kreatif fisika siswa melalui model pembelajaran problem based learning (PBL) pada materi elastisitas. Magister Thesis. Malang: Universitas Negeri Malang.

Batlolona, J R, Singerin, S., & Diantoro, M. (2020). Influence of Problem Based Learning Model on Student Mental Models. Jurnal Pendidikan Fisika Indonesia, 16(1), 14–23. https://doi.org/10.15294/jpfi.v16i1.14253.

Batlolona, John Rafafy. (2019). Creative Thinking Skills Students in Physics on Solid Material Elasticity. Journal of Turkish Science Education, 16(1), 48–61. https://doi.org/10.12973/tused.10265a.

Bigozzi, L., Tarchi, C., Fiorentini, C., Falsini, P., & Stefanelli, F. (2018). The influence of teaching approach on students’ conceptual learning in physics. Frontiers in Psychology, 9(DEC), 1–14. https://doi.org/10.3389/fpsyg.2018.02474.

Canlas, I. P. (2019). Using visual representations in identifying students’ preconceptions in friction. Research in Science and Technological Education, 00(00), 1–29. https://doi.org/10.1080/02635143.2019.1660630.

Carter, J. L., Richards, M., Hotopf, M., & Hatch, S. L. (2019). The roles of non-cognitive and cognitive skills in the life course development of adult health inequalities. Social Science and Medicine, 232, 190–198. https://doi.org/10.1016/j.socscimed.2019.04.041.

Chiou, G. L. (2013). Reappraising the relationships between physics students’ mental models and predictions: An example of heat convection. Physical Review Special Topics - Physics Education Research, 9(1), 1–15.

Chiou, G. L., & Anderson, O. R. (2010). A study of undergraduate physics students’ understanding of heat conduction based on mental model theory and an ontology-process analysis. Science Education, 94(5), 825–854. https://doi.org/10.1002/sce.20385.

Claassen, L., Bostrom, A., & Timmermans, D. R. M. (2016). Focal points for improving communications about electromagnetic fields and health: A mental models approach. Journal of Risk Research, 19(2), 246–269.

Creswell, John W. (2012). Educational research: planning, conducting, evaluating, quantitative and qualitative research (Fourth Edition). United State of America: Pearson Education Inc.

Didiş, N., EryIlmaz, A., & Erkoç, Ş. (2014). Investigating students’ mental models about the quantization of light, energy, and angular momentum. Physical Review Special Topics - Physics Education Research, 10(2), 1–28.

Dring, J. C. (2019). Problem-Based Learning – Experiencing and understanding the prominence during Medical School: Perspective. Annals of Medicine and Surgery, 47, 27–28. https://doi.org/10.1016/j.amsu.2019.09.004.

Fidan, M., & Tuncel, M. (2019). Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education. Computers and Education, 142, 103635. https://doi.org/10.1016/j.compedu.2019.103635.

Gary, M. S., & Wood, R. E. (2016). Unpacking mental models through laboratory experiments. System Dynamics Review, 32(2), 99–127. https://doi.org/10.1002/sdr.1560.

Hake, R. R. 1999. Analyzing change/gain scores, American Educational Research Association, [online] di:http://www.physics.indiana.edu/~sdi/AnalyzingChange-Gain, pdf.

Hrepic, Z., Zollman, D. A., & Rebello, N. S. (2010). Identifying students’ mental models of sound propagation: The role of conceptual blending in understanding conceptual change. Physical Review Special Topics - Physics Education Research, 6(2), 1–18.

Hung, C. Y., Xu, W. W., & Lin, Y. R. (2020). Multi-touch, gesture-based simulations: Impacts on learning optical imaging and mental model development. Computers and Education, 145, 103727. https://doi.org/10.1016/j.compedu.2019.103727.

Ifenthaler, D. (2006). Diagnose lernabhängiger Veränderung mentaler Modelle. Entwicklung der SMD-Technologie als methodologisches Verfahren zur relationalen, strukturellen und semantischen Analyse individueller Modellkonstruktionen. [Diagnosis of the learning-dependent progression of mental models. Development of the SMDTechnology as a methodology for assessing individual models on relational, structural and semantic levels]. Freiburg: Universitäts-Dissertation.

Johnson-Glenberg, M. C., Megowan-Romanowicz, C., Birchfield, D. A., & Savio-Ramos, C. (2016). Effects of embodied learning and digital platform on the retention of physics content: Centripetal force. Frontiers in Psychology, 7, 1–22. https://doi.org/10.3389/fpsyg.2016.01819.

Johnson-Laird, P. N. (2013). Mental models and cognitive change. Journal of Cognitive Psychology, 25(2), 131–138. https://doi.org/10.1080/20445911.2012.759935.

Korganci, N., Miron, C., Dafinei, A., & Antohe, S. (2015). The Importance of Inquiry-Based Learning on Electric Circuit Models for Conceptual Understanding. Procedia - Social and Behavioral Sciences, 191, 2463–2468. https://doi.org/10.1016/j.sbspro.2015.04.530.

Lin, J. W. (2016). Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students’ Preconceptions of Electric Circuits? International Journal of Science and Mathematics Education, 14(1), 287–307. https://doi.org/10.1007/s10763-015-9635-4.

Lin, J. W. (2017). A cross-grade study validating the evolutionary pathway of student mental models in electric circuits. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3099–3137. https://doi.org/10.12973/eurasia.2017.00707a.

Liu, S. C., Lin, H. shyang, & Tsai, C. Y. (2020). Ninth grade students’ mental models of the marine environment and their implications for environmental science education in Taiwan. Journal of Environmental Education, 51(1), 72–82. https://doi.org/10.1080/00958964.2019.1633990.

Lozano, E., Gracia, J., Corcho, O., Noble, R. A., & Gómez-Pérez, A. (2015). Problem-based learning supported by semantic techniques. Interactive Learning Environments, 23(1), 37–54. https://doi.org/10.1080/10494820.2012.745431.

Lycke, K. H., Grøttum, P., & Strømsø, H. (2006). Student learning strategies, mental models and learning outcomes in problem-based and traditional curricula in medicine. Medical Teacher, 28(8), 717–722. https://doi.org/10.1080/01421590601105645.

Oh, J. Y., & Park, S. K. (2014). Understanding pre-service elementary school teachers’ mental models about seasonal change. Journal of Turkish Science Education, 11(3), 3– 20. https://doi.org/10.12973/tused.10115a.

Overton, T. L., & Randles, C. A. (2015). Beyond problem-based learning: Using dynamic PBL in chemistry. Chemistry Education Research and Practice, 16(2), 251–259. https://doi.org/10.1039/c4rp00248b.

ÖZCAN, O. (2013). Investigation of mental models of turkish pre-service physics students for the concept of “spin. Eurasian Journal of Educational Research, 52, 21-36.

Park, E. J., & Light, G. (2009). Identifying Atomic Structure as a Threshold Concept: Student mental models and troublesomeness. International Journal of Science Education, 31(2), 233–258. https://doi.org/10.1080/09500690701675880.

Park, S. K., & Oh, J. Y. (2013). Learners’ ontological categories according to their mental models of plate boundaries. Journal of Turkish Science Education, 10(2), 17–34.

Pasco, D., & Ennis, C. D. (2015). Third-grade students’ mental models of energy expenditure during exercise. Physical Education and Sport Pedagogy, 20(2), 131–143. https://doi.org/10.1080/17408989.2013.803525.

Pendrill, A. (2020). Forces in circular motion : discerning student strategies. Physics Education, 55(4), 1-10.

Pestka, K. A. (2008). Young’s Modulus of a Marshmallow. The Physics Teacher, 46(3), 140– 141. https://doi.org/10.1119/1.2840976.

Phungsuk, R., Viriyavejakul, C., & Ratanaolarn, T. (2017). Development of a problem-based learning model via a virtual learning environment. Kasetsart Journal of Social Sciences, 38(3), 297–306. https://doi.org/10.1016/j.kjss.2017.01.001.

Richardson, J. T. E. (2007). Mental models of learning in distance education. British Journal of Educational Psychology, 77(2), 253–270. https://doi.org/10.1348/000709906X110557 Rubinstein, M., & Panyukov, S. (2002). Elasticity of polymer networks. Macromolecules, 35(17), 6670–6686. https://doi.org/10.1021/ma0203849.

Salta, K., & Koulougliotis, D. (2020). Domain specificity of motivation: chemistry and physics learning among undergraduate students of three academic majors. International Journal of Science Education, 42(2), 253–270. https://doi.org/10.1080/09500693.2019.1708511.

Scott, K. S. (2017). An Integrative Framework for Problem-Based Learning and Action Learning: Promoting Evidence-Based Design and Evaluation in Leadership Development. In Human Resource Development Review (Vol. 16). https://doi.org/10.1177/1534484317693090.

Shen, Z., Tan, S., & Siau, K. (2019). Use of mental models and cognitive maps to understand students’ learning challenges. Journal of Education for Business, 94(5), 281–289. https://doi.org/10.1080/08832323.2018.1527748.

Stains, M., & Sevian, H. (2015). Uncovering Implicit Assumptions: a Large-Scale Study on Students’ Mental Models of Diffusion. Research in Science Education, 45(6), 807–840. https://doi.org/10.1007/s11165-014-9450-x.

Steenkamp, C. M., Rootman-le Grange, I., & Müller-Nedebock, K. K. (2019). Analysing assessments in introductory physics using semantic gravity: refocussing on core concepts and context-dependence. Teaching in Higher Education, 0(0), 1–16. https://doi.org/10.1080/13562517.2019.1692335.

Supasorn, S. (2015). Grade 12 students’ conceptual understanding and mental models of galvanic cells before and after learning by using small-scale experiments in conjunction with a model kit. Chemistry Education Research and Practice, 16(2), 393–407. https://doi.org/10.1039/c4rp00247d.

van Schijndel, T. J. P., van Es, S. E., Franse, R. K., van Bers, B. M. C. W., & Raijmakers, M.

E. J. (2018). Children’s mental models of prenatal development. Frontiers in Psychology, 9, 1–13. https://doi.org/10.3389/fpsyg.2018.01835.

Xiao, F., Barnard-Brak, L., Lan, W., & Burley, H. (2019). Examining problem-solving skills in technology-rich environments as related to numeracy and literacy. International Journal of Lifelong Education, 38(3), 327–338. https://doi.org/10.1080/02601370.2019.1598507.

Yeo, J., & Tan, S. C. (2014). Redesigning problem-based learning in the knowledge creation paradigm for school science learning. Instructional Science, 42(5), 747–775. https://doi.org/10.1007/s11251-014-9317-6

Yew, E. H. J., & Goh, K. (2016). Problem-Based Learning: An Overview of its Process and Impact on Learning. Health Professions Education, 2(2), 75–79. https://doi.org/10.1016/j.hpe.2016.01.004.

Zarkadis, N., Papageorgiou, G., & Stamovlasis, D. (2017). Studying the consistency between and within the student mental models for atomic structure. Chemistry Education Research and Practice, 18(4), 893–902. https://doi.org/10.1039/c7rp00135e.

Zhang, Z. Q., & Yang, J. L. (2015). Improving safety of runway overrun through foamed concrete aircraft arresting system: An experimental study. International Journal of Crashworthiness, 20(5), 448–463. https://doi.org/10.1080/13588265.2015.1033971.