Modelling-based pedagogy as a theme across science disciplines–Effects on scientific reasoning and content understanding

Kathy L. Malone 1 2 * , Anita Schuchardt 3
More Detail
1 University of Hawai’i at Hilo, Hilo, HI, USA
2 Nazarbayev University, Astana, KAZAKHSTAN
3 University of Minnesota–Twin Cities, Minneapolis, MN, USA
* Corresponding Author
EUR J SCI MATH ED, Volume 11, Issue 4, pp. 717-737. https://doi.org/10.30935/scimath/13516
Published Online: 31 July 2023, Published: 01 October 2023
OPEN ACCESS   926 Views   626 Downloads
Download Full Text (PDF)

ABSTRACT

Due to the increased use of scientific models and modelling in K-12 education, there is a need to uncover its effects on students over time. Prior research has shown that the use of scientific modelling in K-12 classes is associated with improved conceptual knowledge and problem-solving skills. However, few studies have explicitly tested the longitudinal benefits of using model-based instruction on students’ scientific reasoning skills (SRS) and content knowledge. This paper studies the effects of the use of modelling-based pedagogy in a longitudinal comparative case study on students’ SRS using hierarchical linear modeling. Our findings showed that initial exposure to modelling-based instruction increased scientific reasoning scores significantly. By the end of their first year of science instruction, the average high school freshman in our study achieved the scientific reasoning level of many undergraduate STEM majors. More importantly, students in the lowest quartile of scientific reasoning demonstrated increased scores over the three years of the modeling-based course sequence. In addition, reasoning scores in the modelling classes were a significant predictor of post-content knowledge in all subjects. Our results suggested that students should be exposed to model-based instruction early and consistently to achieve equity in science instruction.

CITATION

Malone, K. L., & Schuchardt, A. (2023). Modelling-based pedagogy as a theme across science disciplines–Effects on scientific reasoning and content understanding. European Journal of Science and Mathematics Education, 11(4), 717-737. https://doi.org/10.30935/scimath/13516

REFERENCES

  • Aakre, I. G., Persson, J. R., Lein, H. L., & Eggen, P. O. (2021). First-year university students’ perception of chemical bonding and bond energy. Nordic Journal of STEM Education, 4(2), 12-30. https://doi.org/10.5324/njsteme.v4i2.3300
  • Al-Balushi, S. M., Al-Musawi, A. S., Ambusaidi, A. K., & Al-Hajri, F. H. (2017). The effectiveness of interacting with scientific animations in chemistry using mobile devices on grade 12 students’ spatial ability and scientific reasoning skills. Journal of Science Education and Technology, 26(1), 70-81. https://doi.org/10.1007/s10956-016-9652-2
  • Bao, L., Fang, K., Cai, T., Wang, J., Yang, L., Cui, L., Han, J., Ding, L., & Luo, Y. (2009). Learning of content knowledge and development of scientific reasoning ability: A cross culture comparison. American Journal of Physics, 77(12), 1118-1123. https://doi.org/10.1119/1.2976334
  • Bao, L., Xiao, Y., Koenig, K., & Han, J. (2018). Validity evaluation of the Lawson classroom test of scientific reasoning. Physical Review Physics Education Research, 14(2), 020106. https://doi.org/10.1103/PhysRevPhysEducRes.14.020106
  • Barlow, A. T., Frick, T. M., Barker, H. L., & Phelps, A. J. (2014). Modeling instruction: The impact of professional development on instructional practices. Science Educator, 23(1), 14-26.
  • Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2016). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(7), 1082-1112. https://doi.org/10.1002/tea.21257
  • Bernard, P., & Dudek-Różycki, K. (2019). Influence of training in inquiry-based methods on in-service science teachers’ reasoning skills. Chemistry Teacher International, 1(2), 20180023. https://doi.org/10.1515/cti-2018-0023
  • Blumer, L. S., & Beck, C. W. (2019). Laboratory courses with guided-inquiry modules improve scientific reasoning and experimental design skills for the least-prepared undergraduate students. CBE–Life Sciences Education, 18(1), ar2. https://doi.org/10.1187/cbe.18-08-0152
  • Bouzid, T., Kaddari, F., & Darhmaoui, H. (2022). Force and motion misconceptions’ pliability, the case of Moroccan high school students. The Journal of Educational Research, 115(2), 122-132. https://doi.org/10.1080/00220671.2022.2064802
  • Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based teaching and learning with Biologica: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13, 23-41. https://doi.org/10.1023/B:JOST.0000019636.06814.e3
  • Cameron, K., Malone, K. L., Sabree, Z., & Schuchardt, A. (2023). Lazy lizards in a drought: Science modeling and English learners. Science Activities, 1-12. https://doi.org/10.1080/00368121.2023.2200918
  • Carleschi, E., Chrysostomou, A., Cornell, A. S., & Naylor, W. (2022). Probing the effect on student conceptual understanding due to a forced mid-semester transition to online teaching. European Journal of Physics, 43(3), 035702. https://doi.org/10.1088/1361-6404/ac41d9
  • Chang, C. Y. (2010). Does problem-solving=prior knowledge + reasoning skills in earth science? An exploratory study. Research in Science Education, 40(2), 103-116. https://doi.org/10.1007/s11165-008-9102-0
  • Clement, J. J., & Steinberg, M. S. (2002). Step-wise evolution of mental models of electric circuits: A “learning-aloud” case study. The Journal of the Learning Sciences, 11(4), 389-452. https://doi.org/10.1207/S15327809JLS1104_1
  • Coletta, V. P., & Phillips, J. A. (2005). Interpreting FCI scores: Normalized gain, pre-instruction scores and scientific reasoning ability. American Journal of Physics, 73(12), 1172-1182. https://doi.org/10.1119/1.2117109
  • Coletta, V. P., Phillips, J. A., & Steinert, J. J. (2007). Why you should measure your students’ reasoning ability. The Physics Teacher, 45, 235-238. https://doi.org/10.1119/1.2715422
  • College Board. (2020). The SAT subject tests student guide. https://satsuite.collegeboard.org/media/pdf/sat-subject-tests-student-guide.pdf
  • Ding, L. (2014). Verification of causal influences of reasoning skills and epistemology on physics conceptual learning. Physical Review Special Topics–Physics Education Research, 10(2), 023101. https://doi.org/10.1103/PhysRevSTPER.10.023101
  • Ding, L. (2018). Progression trend of scientific reasoning from elementary school to university: A large-scale cross-grade survey among Chinese students. International Journal of Science and Mathematics Education, 16(8), 1479-1498. https://doi.org/10.1007/s10763-017-9844-0
  • Ding, L., Wei, X., & Liu, X. (2016). Variations in university students’ scientific reasoning skills across majors, years, and types of institutions. Research in Science Education, 46(5), 613-632. https://doi.org/10.1007/s11165-015-9473-y
  • Dori, Y. J., & Kaberman, Z. (2012). Assessing high school chemistry students’ modeling sub-skills in a computerized molecular modeling learning environment. Instructional Science, 40, 69-91. https://doi.org/10.1007/s11251-011-9172-7
  • Dukerich, L. (2015). Applying modeling instruction to high school chemistry to improve students’ conceptual understanding. Journal of Chemical Education, 92(8), 1315-1319. https://doi.org/10.1021/ed500909w
  • Dye, J., Cheatham, T., Rowell, G. H., Barlow, A. T., & Carlton, R. (2013). The impact of modeling instruction within the inverted curriculum on student achievement in science. Electronic Journal of Science Education, 17, 1-19.
  • Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: A meta-analysis. Review of Educational Research, 82(3), 300-329. https://doi.org/10.3102/0034654312457206
  • Georgiou, H., & Sharma, M. (2020). Engaging science academics with evidence based practices: Use of concept inventories in chemistry and physics across eight universities. International Journal of Innovation in Science and Mathematics Education, 28(4), 28-43. https://doi.org/10.30722/IJISME.28.04.003
  • Getahun, D. A. (2022). Scientific reasoning among teachers and teacher trainees: The case in Ethiopian schools and teacher training colleges. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-022-10347-6
  • Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71, 742-752. https://doi.org/10.1086/425063
  • Gobert, J. D., O’Dwyer, L., Horwitz, P., Buckley, B. C., Tal Levy, S., & Wilensky, U. (2011) Examining the relationship between students’ understanding of the nature of models and conceptual learning in biology, physics, and chemistry. International Journal of Science Education, 33(5), 653-684. https://doi.org/10.1080/09500691003720671
  • Godfrey-Smith, P. (2006). The strategy of model-based science. Biology & Philosophy, 21, 725-740. https://doi.org/10.1007/s10539-006-9054-6
  • Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64-74. https://doi.org/10.1119/1.18809
  • Halloun, I. A. (2007). Mediated modeling in science education. Science and Education, 16(7), 653-697. https://doi.org/10.1007/s11191-006-9004-3
  • Harrison, A. G., & Treagust, D. F. (2000). Learning about atoms, molecules, and chemical bonds: A case study of multiple-model use in grade 11 chemistry. Science Education, 84(3), 352-381. https://doi.org/10.1002/(SICI)1098-237X(200005)84:3<352::AID-SCE3>3.0.CO;2-J
  • Heilbronner, N. N. (2011). Stepping onto the STEM pathway factors affecting talented students’ declaration of STEM majors in college. Journal for the Education of the Gifted, 34(6), 876-899. https://doi.org/10.1177/0162353211425100
  • Hestenes D., Wells, M., & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30(3), 141-158. https://doi.org/10.1119/1.2343497
  • Hestenes, D. (2010). Modeling theory for math and science education. In R. Lesh, C. R. Haines, P. L. Galbraith, & A. Harford (Eds.), Modeling students’ mathematical modeling competencies (pp. 13-41). Springer. https://doi.org/10.1007/978-1-4419-0561-1_3
  • Hester, S. D., Nadler, M., Katcher, J., Elfring, L. K., Dykstra, E., Rezende, L. F., & Bolger, M. S. (2018). Authentic inquiry through modeling in biology (AIM-Bio): An introductory laboratory curriculum that increases undergraduates’ scientific agency and skills. CBE–Life Sciences Education, 17(4), ar63. https://doi.org/10.1187/cbe.18-06-0090
  • Illes, M., Wilson, P., & Bruce, C. (2019). Forensic epistemology: Testing the reasoning skills of crime scene experts. Canadian Society of Forensic Science Journal, 52(4), 151-173. https://doi.org/10.1080/00085030.
  • 2019.1664260
  • Jenkins, J. L., & Howard, E. M. (2019). Implementation of modeling instruction in a high school chemistry unit on energy and states of matter. Science Education International, 30(2), 97-104. https://doi.org/10.33828/sei.v30.i2.3
  • Jensen, J. L., Neeley, S., Hatch, J. B., & Piorczynski, T. (2015). Learning scientific reasoning skills may be key to retention in science, technology, engineering, and mathematics. Journal of College Student Retention: Research, Theory & Practice, 19(2), 126-144. https://doi.org/10.1177/1521025115611616
  • Kaygisiz, G. M., Gurkan, B., & Akbas, U. (2018). Adaptation of scientific reasoning scale into Turkish and examination of its psychometric properties. Educational Sciences: Theory and Practice, 18(3), 737-757.
  • KMK. (Ed.). (2005). Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss [Biology education standards for the Mittlere Schulabschluss]. Wolters Kluwer.
  • Kuhn, D., & Dean, Jr, D. (2004). Metacognition: A bridge between cognitive psychology and educational practice. Theory into Practice, 43(4), 268-273. https://doi.org/10.1207/s15430421tip4304_4
  • Lawson, A. E. (1992). What do tests of “formal” reasoning actually measure? Journal of Research in Science Teaching, 29(9), 965-983. https://doi.org/10.1002/tea.3660290906
  • Lawson, A. E. (2004). The nature and development of scientific reasoning. International Journal of Science and Mathematics Education, 2(3), 307-338. https://doi.org/10.1007/s10763-004-3224-2
  • Lawson, A. E., Banks, D. L., & Logvin, M. (2007). Self-efficacy, reasoning ability and achievement in college biology. Journal of Research in Science Teaching, 44(5), 706-724. https://doi.org/10.1002/tea.20172
  • Lawson, A. E., Clark, B., Meldrum, E. C., Falconer, K. A., Sequist, J. M., & Kwon, Y. J. (2000). Development of scientific reasoning in college biology: Do two levels of general hypothesis-testing skills exist? Journal of Research in Science Teaching, 37(1), 81-101. https://doi.org/10.1002/(SICI)1098-2736(200001)37:1<81::AID-TEA6>3.0.CO;2-I
  • Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy. In K. A. Renninger, I. E. Sigel, W. Damon, & R. M. Lerner (Eds.), Handbook of child psychology: Child psychology in practice (pp. 153-196). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470147658.chpsy0405
  • Lehrer, R., & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96(4), 701-724. https://doi.org/10.1002/sce.20475
  • Liang, L. L., Fulmer, G. W., Majerich, D. M., Clevenstine, R., & Howanski, R. (2012). The effects of a model-based physics curriculum program with a physics first approach: A causal-comparative study. Journal of Science Education and Technology, 21(1), 114-124. https://doi.org/10.1007/s10956-011-9287-2
  • Malone, K. (2008). Correlations among knowledge structures, force concept inventory, and problem-solving behaviors. Physics Review Special Topics Physics Education Research, 4, 020107. https://doi.org/10.1103/PhysRevSTPER.4.020107
  • Malone, K. L., & Schuchardt, A. (2016, January). The efficacy of modeling instruction in chemistry: A case study. In Proceedings from HICE 2016: The 14th Annual Hawaii International Conference on Education (pp. 1513-1518). Honolulu, HI.
  • Malone, K. L., & Schuchardt, A. (2020). Population growth modelling simulations: Do they affect the scientific reasoning abilities of students? In H. C. Lane, Sl. Zvacek, & J. Uhomoibhi (Eds.), Computer supported education: 11th International Conference, CSEDU 2019, Heraklion, Crete, May 2-4, 2019, Revised Selected Papers, in Communications in Computer and Information Sciences Series (Vol. 1022, pp. 285-307). Springer Nature. https://doi.org/10.1007/978-3-030-58459-7_14
  • Malone, K. L., Schuchardt A. M., & Sabree, Z. (2019). Models and modeling in evolution. In U. Harms & M. J. Reiss (Eds.), Evolution education re-considered: Understanding what works (pp. 207-226). Springer International Publishing. https://doi.org/10.1007/978-3-030-14698-6
  • Malone, K. L. (2023). The effects of modeling‐based pedagogy on conceptual understanding, scientific reasoning skills, and attitudes towards science of English Learners. Science Education, 1-33. https://doi.org/10.1002/sce.21805
  • Mehl, C. E. (2022). Student experience and outcomes of chemistry modeling instruction [Doctoral dissertation, The Ohio State University].
  • Ministry of Education in Taiwan. (2018). Curriculum standards for grades 1-12. Ministry of Education. https://www.naer.edu.tw/
  • Ministry of Education. (2014). Folkeskoleloven [The Education Act]. https://www.retsinformation.dk/forms/r0710.aspx?id=176327
  • Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry‐based science instruction–What is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474-496. https://doi.org/10.1002/tea.20347
  • Moore, J. C., & Rubbo, L. J. (2012). Scientific reasoning abilities of nonscience majors in physics-based courses. Physical Review Special Topics–Physics Education Research, 8(1), 10106. https://doi.org/10.1103/PhysRevSTPER.8.010106
  • Mulford, D. R., & Robinson, W. R. (2002). An inventory for alternate conceptions among first-semester general chemistry students. Journal of Chemical Education, 79(6), 739-744. https://doi.org/10.1021/ed079p739
  • National Research Council. (2015). Guide to implementing the next generation science standards. Board on Science Education, Division of Behavioral and Social Sciences and Education. https://nap.nationalacademies.org/catalog/18802/guide-to-implementing-the-next-generation-science-standards
  • NGSS Lead States. 2013. Next generation science standards: For states, by states. The National Academies Press.
  • Nieminen, P., Savinainen, A., & Viiri, J. (2012). Relations between representational consistency, conceptual understanding of the force concept, and scientific reasoning. Physical Review Special Topics–Physics Education Research, 8(1), 010123. https://doi.org/10.1103/PhysRevSTPER.8.010123
  • Norris, S. P., Phillips, L. M., & Korpan, C. A. (2003). University students’ interpretation of media reports of science and its relationship to background knowledge, interest, and reading difficulty. Public Understanding of Science, 12(2), 123-145. https://doi.org/10.1177/09636625030122001
  • OECD. (2018). PISA 2018 results (volume I): What students know and can do. PISA, OECD Publishing. https://doi.org/10.1787/5f07c754-en
  • Opitz, A., Heene, M., & Fischer, F. (2017) Measuring scientific reasoning–a review of test instruments. Educational Research and Evaluation, 23(3-4), 78-101. https://doi.org/10.1080/13803611.2017.1338586
  • Orosz, G., Németh, V., Kovács, L., Somogyi, Z., & Korom, E. (2023). Guided inquiry-based learning in secondary-school chemistry classes: A case study. Chemistry Education Research and Practice, 24(1), 50-70. https://doi.org/10.1039/D2RP00110A
  • Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), 177-196. https://doi.org/10.1007/s10972-014-9384-1
  • Passmore, C., & Stewart, J. (2002). A modeling approach to teaching evolutionary biology in high schools. Journal of Research in Science Teaching, 39(3), 185-204. https://doi.org/10.1002/tea.10020
  • Passmore, C., Stewart, J., & Cartier, J. (2009). Model-based inquiry and school science: Creating connections. School Science and Mathematics, 109(7), 394-402. https://doi.org/10.1111/j.1949-8594.2009.tb17870.x
  • Penuel, W. R., Fishman, B. J., Cheng, B. H., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational Researcher, 40(7), 331-337. https://doi.org/10.3102/0013189X11421826
  • Posthuma-Adams, E. (2014). How the chemistry modeling curriculum engages students in seven science practices outlined by the college board. Journal of Chemical Education, 91(9), 1284-1290. https://doi.org/10.1021/ed400911a
  • Raudenbusch, S. W., & Byrk, A. (2002). Hierarchical linear models: Applications and data analysis methods. SAGE.
  • Russ, R. S., & Odden, T. O. B. (2017). Intertwining evidence-and model-based reasoning in physics sensemaking: An example from electrostatics. Physical Review Physics Education Research, 13(2), 020105. https://doi.org/10.1103/PhysRevPhysEducRes.13.020105
  • Russ, R. S., Coffey, J. E., Hammer, D., & Hutchison, P. (2009). Making classroom assessment more accountable to scientific reasoning: A case for attending to mechanistic thinking. Science Education, 93(5), 875-891. https://doi.org/10.1002/sce.20320
  • Sapia, P., Napoli, F., & Bozzo, G. (2022). The Lawson’s test for scientific reasoning as a predictor for university formative success: A prospective study. Education Sciences, 12(11), 814. https://doi.org/10.3390/educsci12110814
  • Schinka, J. A., Velicer, W. F., & Weiner, I. B. (2003). Handbook of psychology: Research methods in psychology. John Wiley & Sons, Inc.
  • Schunk, D. H., Meece, J. R., & Pintrich, P. R. (2012). Motivation in education: Theory, research, and applications. Pearson.
  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654. https://doi.org/10.1002/tea.20311
  • Svoboda, J., & Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119-142. https://doi.org/10.1007/s11191-011-9425-5
  • Thompson, E. D., Bowling, B. V., & Markle, R. E. (2018). Predicting student success in a major’s introductory biology course via logistic regression analysis of scientific reasoning ability and mathematics scores. Research in Science Education, 48(1), 151-163. https://doi.org/10.1007/s11165-016-9563-5
  • Vanlaar, G., Kyriakides, L., Panayiotou, A., Vandecandelaere, M., McMahon, L., De Fraine, B., & Van Damme, J. (2016). Do the teacher and school factors of the dynamic model affect high-and low-achieving student groups to the same extent? A cross-country study. Research Papers in Education, 31(2), 183-211. https://doi.org/10.1080/02671522.2015.1027724
  • Williams, K. R., Wasson, S. R., Barrett, A., Greenall, R. F., Jones, S. R., & Bailey, E. G. (2021). Teaching Hardy-Weinberg equilibrium using population-level Punnett squares: Facilitating calculation for students with math anxiety. CBE–Life Sciences Education, 20(2), ar22. https://doi.org/10.1187/cbe.20-09-0219
  • Yanto, B. E., Subali, B., & Suyanto, S. (2019). Improving students’ scientific reasoning skills through the three levels of inquiry. International Journal of Instruction, 12(4), 689-704. https://doi.org/10.29333/iji.2019.12444a
  • Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review, 20(1), 99-149. https://doi.org/10.1006/drev.1999.0497