Effectiveness of a model-based inquiry instructional sequence in overcoming students’ teaching-learning difficulties on plant nutrition

References

• R Core Team. (2019). R: A language and environment for statistical computing (R-3.6.3). R Foundation for Statistical Computing.
   Google Scholar
• Abd-El-Khalick, F., BouJaoude, S., Duschl, R., Lederman, N. G., Mamlok-Naaman, R., Hofstein, A., Niaz, M., Treagust, D., & Tuan, H. (2004). Inquiry in science education: International perspectives. Science Education, 88(3), 397–419. https://doi.org/10.1002/sce.10118
   Web of Science ®Google Scholar
• Anderson, R. D. (2002). Reforming science teaching: What research says about inquiry. Journal of Science Teacher Education, 13(1), 1–12. https://doi.org/10.1023/A:1015171124982
   Google Scholar
• Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13(1), 1–14. https://doi.org/10.1207/s15327809jls1301_1
   Web of Science ®Google Scholar
• Bevins, S., & Price, G. (2016). Reconceptualising inquiry in science education. International Journal of Science Education, 38(1), 17–29. https://doi.org/10.1080/09500693.2015.1124300
   Web of Science ®Google Scholar
• Brown, M. H., & Schwartz, R. S. (2009). Connecting photosynthesis and cellular respiration: Preservice teachers’ conceptions. Journal of Research in Science Teaching, 46(7), 791–812. https://doi.org/10.1002/tea.20287
   Web of Science ®Google Scholar
• Bryce, C. M., Baliga, V. B., De Nesnera, K. L., Fiack, D., Goetz, K., Tarjan, L. M., Wade, C. E., Yovovich, V., Baumgart, S., Bard, D. G., Ash, D., Parker, I. M., & Gilbert, G. S. (2016). Exploring models in the biology classroom. The American Biology Teacher, 78(1), 35–42. https://doi.org/10.1525/abt.2016.78.1.35
   Web of Science ®Google Scholar
• Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Powell, J. C., Westbrook, A., & Landes, N.. (2006). The BSCS 5E instructional model: Origins, effectiveness, and applications. Biological Sciences Curriculum Study (BSCS).
   Google Scholar
• Campbell, T., Zhang, D., & Neilson, D. (2011). Model based inquiry in the high school physics classroom: An exploratory study of implementation and outcomes. Journal of Science Education and Technology, 20(3), 258–269. https://doi.org/10.1007/s10956-010-9251-6
   Web of Science ®Google Scholar
• Chattergoon, R. (2020). Using polytomous item response theory models to validate learning progressions [doctoral dissertation]. University of Colorado. CU Scholar. https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/79407z15w
   Google Scholar
• Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053. https://doi.org/10.1080/095006900416901
   Web of Science ®Google Scholar
• Çokadar, H. (2012). Photosynthesis and respiration processes: Prospective teachers’ conception levels. Education and Science, 37(164), 81–93.
   Web of Science ®Google Scholar
• Coll, R. K., France, B., & Taylor, I. (2005). The role of models/and analogies in science education: Implications from research. International Journal of Science Education, 27(2), 183–198. https://doi.org/10.1080/0950069042000276712
   Web of Science ®Google Scholar
• Couso, D., & Garrido-Espeja, A. (2017). Models and modelling in pre-service teacher education: Why we need both. In K. Hahl, K. Juuti, J. Lampiselkä, A. Uitto, & J. Lavonen (Eds.), Cognitive and affective aspects in science education research (Vol. 3, pp. 245–261). Springer.
   Google Scholar
• Crawford, B. A. (2014). From inquiry to scientific practices in the science classroom. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (Vol. Vol. 2 (1st ed., pp. 512–540). Lawrence Erlbaum.
   Google Scholar
• Duschl, R. (2008). Science education in three-part harmony: Balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32(1), 268–291. https://doi.org/10.3102/0091732X07309371
   Web of Science ®Google Scholar
• Düsing, K., Asshoff, R., & Hammann, M. (2019). Students’ conceptions of the carbon cycle: Identifying and interrelating components of the carbon cycle and tracing carbon atoms across the levels of biological organisation. Journal of Biological Education, 53(1), 110–125. https://doi.org/10.1080/00219266.2018.1447002
   Web of Science ®Google Scholar
• Ebert-May, D., Batzli, J., & Lim, H. (2003). Disciplinary research strategies for assessment of learning. BioScience, 53(12), 1221–1228. https://doi.org/10.1641/0006-3568(2003)053[1221:DRSFAO]2.0.CO;2
   Web of Science ®Google Scholar
• Feldon, D. F., & Tofel-Grehl, C. (2022). Phenomenography as a basis for fully integrated mixed methodologies. In J. H. Hitchcock & A. J. Onwuegbuzie (Eds.), The Routledge handbook for advancing integration in mixed methods research (pp. 124–138). Routledge.
   Google Scholar
• García-Carmona, A. (2020). From inquiry-based science education to the approach based on scientific practices. Science & Education, 29(2), 443–463. https://doi.org/10.1007/s11191-020-00108-8
   Web of Science ®Google Scholar
• Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752. https://doi.org/10.1086/425063
   Web of Science ®Google Scholar
• Gilbert, J. K., & Boulter, C. J. (2000). Developing models in science education. Springer.
   Google Scholar
• Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education (1st ed., Vol. 9). Springer.
   Google Scholar
• Guisasola, J., Ametller, J., & Zuza, K. (2021). Investigación basada en el diseño de Secuencias de Enseñanza-Aprendizaje: Una línea de investigación emergente en Enseñanza de las Ciencias [Design-based research of teaching-learning sequences: A emergent research line in science education]. Revista Eureka Sobre Enseñanza y Divulgación de Las Ciencias, 18(1), 1–18. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2021.v18.i1.1801
   Google Scholar
• Han, F., & Ellis, R. A. (2019). Using phenomenography to tackle key challenges in science education. Frontiers in Psychology, 10, 1–10.https://doi.org/10.3389/fpsyg.2019.01414.
   PubMed Web of Science ®Google Scholar
• Harlen, W. (2010). Principles and big ideas of science education. The Association for Science Education.
   Google Scholar
• Hartley, L. M., Wilke, B. J., Schramm, J. W., D’Avanzo, C., & Anderson, C. W. (2011). College students’ understanding of the carbon cycle: Contrasting principle-based and informal reasoning. BioScience, 61(1), 65–75. https://doi.org/10.1525/bio.2011.61.1.12
   Web of Science ®Google Scholar
• Heritage, M. (2008). Learning progressions: Supporting instruction and formative assessment. Council of Chief State School Officers (CCSSO).
   Google Scholar
• Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368
   Web of Science ®Google Scholar
• Jiménez-Aleixandre, M. P., & Crujeiras, B. (2017). Epistemic practices and scientific practices in science education. In K. S. Taber & B. Akpan (Eds.), Science education (pp. 69–80). Sense.
   Google Scholar
• Jimenez-Liso, M. R., Bellocchi, A., Martinez-Chico, M., & Lopez-Gay, R. (2022). A model-based inquiry sequence as a heuristic to evaluate students’ emotional, behavioural, and cognitive engagement. Research in Science Education, 52(4), 1313–1334. https://doi.org/10.1007/s11165-021-10010-0
   Web of Science ®Google Scholar
• Jiménez-Liso, M. R., López-Banet, L., & Dillon, J. (2020). Changing how we teach acid-base chemistry. Science & Education, 29(5), 1291–1315. https://doi.org/10.1007/s11191-020-00142-6
   Web of Science ®Google Scholar
• Jin, H., Zhan, L., & Anderson, C. W. (2013). Developing a fine-grained learning progression framework for carbon-transforming processes. International Journal of Science Education, 35(10), 1663–1697. https://doi.org/10.1080/09500693.2013.782453
   Web of Science ®Google Scholar
• Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369–387. https://doi.org/10.1080/09500690110110142
   Web of Science ®Google Scholar
• Juuti, K., Lavonen, J., & Melsalo, V. (2016). Pragmatic design-based research – designing as a shared activity of teachers and researches. In D. Psillos & P. Kariotoglou (Eds.), Iterative design of teaching-learning sequences: Introducing the science of materials in European schools (pp. 35–46). Springer Netherlands.
   Google Scholar
• Khan, S. (2007). Model-based inquiries in chemistry. Science Education, 91(6), 877–905. https://doi.org/10.1002/sce.20226
   Web of Science ®Google Scholar
• Köse, S. (2008). Diagnosing student misconceptions: Using drawings as a research method. World Applied Sciences Journal, 3(2), 283–293.
   Google Scholar
• Lin, C., & Hu, R. (2003). Students’ understanding of energy flow and matter cycling in the context of the food chain, photosynthesis, and respiration. International Journal of Science Education, 25(12), 1529–1544. https://doi.org/10.1080/0950069032000052045
   Web of Science ®Google Scholar
• Lupión-Cobos, T., López-Castilla, R., & Blanco-López, Á. (2017). What do science teachers think about developing scientific competences through context-based teaching? A case study. International Journal of Science Education, 39(7), 937–963. https://doi.org/10.1080/09500693.2017.1310412
   Web of Science ®Google Scholar
• Marmaroti, P., & Galanopoulou, D. (2006). Pupils’ understanding of photosynthesis: A questionnaire for the simultaneous assessment of all aspects. International Journal of Science Education, 28(4), 383–403. https://doi.org/10.1080/09500690500277805
   Web of Science ®Google Scholar
• Marton, F. (1981). Phenomenography – Describing conceptions of the world around us. Instructional Science, 10(2), 177–200. https://doi.org/10.1007/BF00132516
   Web of Science ®Google Scholar
• McLaughlin, C. A., & MacFadden, B. J. (2014). At the elbows of scientists: Shaping science teachers’ conceptions and enactment of inquiry-based instruction. Research in Science Education, 44(6), 927–947. https://doi.org/10.1007/s11165-014-9408-z
   Web of Science ®Google Scholar
• Méheut, M., & Psillos, D. (2004). Teaching–learning sequences: Aims and tools for science education research. International Journal of Science Education, 26(5), 515–535. https://doi.org/10.1080/09500690310001614762
   Web of Science ®Google Scholar
• Messig, D., & Groß, J. (2018). Understanding plant nutrition – the genesis of students’ conceptions and the implications for teaching photosynthesis. Education Sciences, 8(3), 132. https://doi.org/10.3390/educsci8030132
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Mohan, L., Chen, J., & Anderson, C. W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46(6), 675–698. https://doi.org/10.1002/tea.20314
   Web of Science ®Google Scholar
• National Research Council [NRC]. (1996). National science education standards. The National Academies Press.
   Google Scholar
• NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.
   Google Scholar
• Nuffield Foundation. (2013). Model-based inquiry and practical work – An introduction. https://www.stem.org.uk/resources/elibrary/resource/459821/model-based-inquiry
   Google Scholar
• Organization for Economic Co-operation and Development [OECD]. (2019). PISA 2018 assessment and analytical framework. OECD Publishing.
   Google Scholar
• 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
   Google Scholar
• Özay, E., & Öztaş, H. (2003). Secondary students’ interpretations of photosynthesis and plant nutrition. Journal of Biological Education, 37(2), 68–70. https://doi.org/10.1080/00219266.2003.9655853
   Web of Science ®Google Scholar
• Pany, P., Meier, F. D., Dünser, B., Yanagida, T., Kiehn, M., & Möller, A. (2022). Measuring students’ plant awareness: A prerequisite for effective botany education. Journal of Biological Education, 1–14. https://doi.org/10.1080/00219266.2022.2159491
   Web of Science ®Google Scholar
• Parker, J. M., Anderson, C. W., Heidemann, M., Merrill, J., Merritt, B., Richmond, G., & Urban-Lurain, M. (2012). Exploring undergraduates’ understanding of photosynthesis using diagnostic question clusters. CBE—Life Sciences Education, 11(1), 47–57. https://doi.org/10.1187/cbe.11-07-0054
   PubMed Web of Science ®Google Scholar
• 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
   Google Scholar
• Pedaste, M., Mäeots, M., Siiman, L. A., de Jong, T., van Riesen, S. A. N., Kamp, E. T., Manoli, C. C., Zacharia, Z. C., & Tsourlidaki, E. (2015). Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational Research Review, 14, 47–61. https://doi.org/10.1016/j.edurev.2015.02.003
   Web of Science ®Google Scholar
• Pedrera, O., Barrutia, O., & Díez, J. R. (2023). Modelo científico de la nutrición vegetal: Análisis epistemológico y propuesta de progresión de aprendizaje [Scientific model of plant nutrition: epistemological analysis and a learning progression proposal]. Revista Eureka Sobre Enseñanza y Divulgación de Las Ciencias, 20(3), 310201–310219. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2023.v20.i3.3102
   Google Scholar
• Pedrera, O., Barrutia, O., & Díez, J. R. (n.d.-a). Do textbooks provide opportunities to develop meaningful botanical literacy? A case study of the scientific model of plant nutrition [under review].
   Google Scholar
• Pedrera, O., Barrutia, O., & Díez, J. R. (n.d.-b). Rooting out teaching-learning difficulties of plant nutrition: A systematic literature review [under review].
   Google Scholar
• Pedrera, O., Barrutia, O., & Díez, J. R. (n.d.-c). Unveiling students’ mental models and learning demands: An empirical validation of secondary students’ model progression on plant nutrition [under review].
   Google Scholar
• Rocard, M., Csermely, P., Jorde, D., Lenzen, D., Walberg, H., & Hemmo, V. (2007). Science education now: A renewed pedagogy for the future of Europe. Directorate General for Research, Science, Economy and Society.
   Google Scholar
• Sandoval, W. (2014). Conjecture mapping: An approach to systematic educational design research. Journal of the Learning Sciences, 23(1), 18–36. https://doi.org/10.1080/10508406.2013.778204
   Web of Science ®Google Scholar
• Schramm, J. W., Jin, H., Keeling, E. G., Johnson, M., & Shin, H. J. (2018). Improved student reasoning about carbon-transforming processes through inquiry-based learning activities derived from an empirically validated learning progression. Research in Science Education, 48(5), 887–911. https://doi.org/10.1007/s11165-016-9584-0
   Web of Science ®Google Scholar
• Schwarz, C. (2009). Developing preservice elementary teachers’ knowledge and practices through modeling-centered scientific inquiry. Science Education, 93(4), 720–744. https://doi.org/10.1002/sce.20324
   Web of Science ®Google Scholar
• Scott, E. E., Wenderoth, M. P., & Doherty, J. H. (2020). Design-based research: A methodology to extend and enrich biology education research. CBE—Life Sciences Education, 19(3), 1–12. https://doi.org/10.1187/cbe.19-11-0245
   Web of Science ®Google Scholar
• Stern, L., & Roseman, J. E. (2004). Can middle-school science textbooks help students learn important ideas? Findings from project 2061s curriculum evaluation study: Life science. Journal of Research in Science Teaching, 41(6), 538–568. https://doi.org/10.1002/tea.20019
   Web of Science ®Google Scholar
• Thompson, S. L., Lotter, C., Fann, X., & Taylor, L. (2016). Enhancing elementary pre-service teachers’ plant processes conceptions. Journal of Science Teacher Education, 27(4), 439–463. https://doi.org/10.1007/s10972-016-9469-0
   Web of Science ®Google Scholar
• Treagust, D. F., & Duit, R. (2008). Conceptual change: A discussion of theoretical, methodological and practical challenges for science education. Cultural Studies of Science Education, 3(2), 297–328. https://doi.org/10.1007/s11422-008-9090-4
   Google Scholar
• Ummels, M. H. J., Kamp, M. J. A., De Kroon, H., & Boersma, K. T. (2015). Promoting conceptual coherence within context-based biology education. Science Education, 99(5), 958–985. https://doi.org/10.1002/sce.21179
   Web of Science ®Google Scholar
• Vosniadou, S. (2019). The development of students’ understanding of science. Frontiers in Education, 4, 32. https://doi.org/10.3389/feduc.2019.00032
   Google Scholar
• Wandersee, J. H. (1983). Students’ misconceptions about photosynthesis: A across-age study. In H. Helm & J. D. Novak (Eds.), Proceedings of the international seminar on misconceptions in science and mathematics (pp. 441–463). Cornell University.
   Google Scholar
• Willard, T.. (2020). The NTSA atlas of the three dimensions. National Science Teaching Association.
   Google Scholar
• Wilson, C. D., Anderson, C. W., Heidemann, M., Merrill, J. E., Merritt, B. W., Richmond, G., Sibley, D. F., & Parker, J. M. (2006). Assessing students’ ability to trace matter in dynamic systems in cell biology. CBE—Life Sciences Education, 5(4), 323–331. https://doi.org/10.1187/cbe.06-02-0142
   PubMedGoogle Scholar
• Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. Science Education, 92(5), 941–967. https://doi.org/10.1002/sce.20259
   Web of Science ®Google Scholar
• Wright, B. D. (2003). Rack and stack: Time 1 vs. Time 2 or pre-test vs. post-test. Rasch Measurement Transactions, 17(1), 905–906.
   Google Scholar