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Integrative Analysis Using Big Ideas: Energy Transfer and Cellular Respiration

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Big ideas in science education are meant to be interpretive frameworks that empower student learning. Unfortunately, outside of the broad conception of scientific evaluation, there are few theoretical explanations of how this might happen. Therefore, we contribute one such explanation, an instructional concept called integrative analysis wherein students use a big idea to interconnect isolated scenarios and enrich their meanings. We illustrate the characteristics and value of integrative analysis within an empirical study of student learning in 9th-grade biology. The study focused on using energy transfer as a big idea for teaching cellular respiration. Fifty-nine students were randomly assigned to one of two instructional conditions. In the “analysis” condition, students processed a set of three manipulatives representing cellular respiration molecules; then, they abstracted the deep energy transfer structure of these manipulatives as a big idea. In the “recognition” condition, students processed the same molecule-manipulatives, but without energy interpretations. Instead, they constructed additional manipulatives using novel materials. Then, students in both conditions received an identical lesson where they used their knowledge of the manipulatives to learn about one cellular respiration process, glycolysis. Specifically, students processed a sequence of three texts describing glycolysis, annotating the texts with either their deep energy transfer structure (analysis condition) or their contextualized knowledge of the manipulatives (recognition condition). A posttest showed that in the analysis condition, this process was significantly integrative as evidenced by analysis students’ advantage over recognition students in connecting glycolysis to novel phenomena and generating causal explanations about glycolysis.

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Data Availability

The data sets for the current study are not publicly available due to the fact that they are part of research in progress. However, they will be made available from the corresponding author on reasonable request.


  1. We followed Windschitl et al.’s (2012) preference for students to understand big ideas as models. This approach affords big ideas epistemological status with students (i.e., imperfect, fitting distinct purposes, subject to revision) while also providing tools with which to develop and communicate big ideas.

  2. A major issue with this energy aspect of the manipulatives—which also extended to the model that students constructed from them—was that it incorrectly represented the effort to form molecules as pushing particles together against repulsive forces between them. From a chemistry standpoint, this is exactly wrong, as the forces involved are attractive, so it takes energy to pull particles away from each other. See the limitations in “Discussion” for additional explanation of this issue and how it limits the conclusions of the study.

  3. Correct student responses did not have to be characteristic of their condition.

  4. Researchers classify this way of thinking as a misconception based on evidence of its persistence through instruction, though some are circumspect about why this is so (Teichert & Stacy, 2002).

  5. We want to thank an anonymous reviewer for pointing out this possibility.sssss.


  • American Association for the Advancement of Science. (1990). Science for all Americans. Oxford University Press.

  • Australian Curriculum Assessment and Reporting Authority [ACARA]. (2012). Shape of the Australian science curriculum. Retrieved May 15, 2021, from

  • Barker, V., & Millar, R. (2000). Students’ reasoning about basic chemical thermodynamics and chemical bonding: What changes occur during a context-based post-16 chemistry course? International Journal of Science Education, 22(11), 1171–1200.

    Google Scholar 

  • Beaney, M. (2014). Analysis. In E. Zalta (Ed.), The Stanford encyclopedia of philosophy (Summer 2021 Edition).

  • Boo, H. K. (1998). Students’ understandings of chemical bonds and the energetics of chemical reactions. Journal of Research in Science Teaching, 35(5), 569–581.

    Google Scholar 

  • Box, G. E. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799.

    Google Scholar 

  • Buxton, C., Harman, R., Cardozo-Gaibisso, L., Jiang, L., Bui, K., & Allexsaht-Snider, M. (2019). Understanding science and language connections: New approaches to assessment with bilingual learners. Research in Science Education, 49(4), 977–988.

    Google Scholar 

  • Capps, D. K., & Shemwell, J. T. (2020). Moving beyond the model as a copy problem: Investigating the utility of teaching about structure-preserving transformations in the model-referent relationship. International Journal of Science Education, 42(12), 2008–2031.

    Google Scholar 

  • Castro-Faix, M., Duncan, R. G., & Choi, J. (2021). Data-driven refinements of a genetics learning progression. Journal of Research in Science Teaching, 58(1), 3–39.

    Google Scholar 

  • Chalmers, C., Carter, M. L., Cooper, T., & Nason, R. (2017). Implementing “big ideas” to advance the teaching and learning of science, technology, engineering, and mathematics (STEM). International Journal of Science and Mathematics Education, 15(1), 25–43.

    Google Scholar 

  • Chase, C. C., Malkiewich, L., & Kumar, S. A. (2019). Learning to notice science concepts in engineering activities and transfer situations. Science Education, 103(2), 440–471.

    Google Scholar 

  • Cheng, M. F., & Brown, D. E. (2015). The role of scientific modeling criteria in advancing students’ explanatory ideas of magnetism. Journal of Research in Science Teaching, 52(8), 1053–1081.

    Google Scholar 

  • Chi, M. T., & VanLehn, K. A. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47(3), 177–188.

    Google Scholar 

  • Clement, J. (2008). The role of explanatory models in teaching for conceptual change. International Handbook of Research on Conceptual Change, 1, 417–452.

    Google Scholar 

  • 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.

    Google Scholar 

  • Close, H. G., & Scherr, R. E. (2015). Enacting Conceptual Metaphor through Blending: Learning activities embodying the substance metaphor for energy. International Journal of Science Education, 37(5–6), 839–866.

    Google Scholar 

  • College Board. (2011). AP biology curriculum Framework 2012–2013. Retrieved from

  • College Board. (2019). AP biology course and exam description effective Fall 2020. Retrieved from

  • Cooper, M. M. (2020). The Crosscutting Concepts: Critical Component or “Third Wheel” of Three-Dimensional Learning? Journal of Chemical Education, 97(4), 903–909.

    Google Scholar 

  • Cooper, M. M., & Klymkowsky, M. W. (2013). The trouble with chemical energy: Why understanding bond energies requires an interdisciplinary systems approach. CBE—Life Sciences Education, 12(2), 306–312.

  • Damşa, C. I., Kirschner, P. A., Andriessen, J. E., Erkens, G., & Sins, P. H. (2010). Shared epistemic agency: An empirical study of an emergent construct. The Journal of the Learning Sciences, 19(2), 143–186.

    Google Scholar 

  • DiSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2–3), 105–225.

    Google Scholar 

  • Dreyfus, B. W., Gouvea, J., Geller, B. D., Sawtelle, V., Turpen, C., & Redish, E. F. (2014). Chemical energy in an introductory physics course for the life sciences. American Journal of Physics, 82(5), 403–411.

    Google Scholar 

  • Duit, R. (1987). Should energy be illustrated as something quasi-material? International Journal of Science Education, 9(2), 139–145.

    Google Scholar 

  • Fang, Z. (2006). The language demands of science reading in middle school. International Journal of Science Education, 28(5), 491–520.

    Google Scholar 

  • Ford, M. (2008). Disciplinary authority and accountability in scientific practice and learning. Science Education, 92(3), 404–423.

    Google Scholar 

  • Fortus, D., Kubsch, M., Bielik, T., Krajcik, J., Lehavi, Y., Neumann, K., & Touitou, I. (2019). Systems, transfer, and fields: Evaluating a new approach to energy instruction. Journal of Research in Science Teaching, 56(10), 1341–1361.

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170.

    Google Scholar 

  • Gentner, D. (2010). Bootstrapping the mind: Analogical processes and symbol systems. Cognitive Science, 34(5), 752–775.

    Google Scholar 

  • Gentner, D., & Kurtz, K. (2005). Learning and using relational categories. In W. K. Ahn, R. L. , Love, B.C., Markman, A.B., & Wolff, P.W. (Eds.), Categorization inside and outside the lab. Washington, DC: American Psychological Association.

  • Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–408.

    Google Scholar 

  • Gentner, D., & Markman, A. B. (2006). Defining structural similarity. The Journal of Cognitive Science, 6(1), 1–20.

    Google Scholar 

  • Gericke, N., Hagberg, M., & Jorde, D. (2013). Upper secondary students’ understanding of the use of multiple models in biology textbooks—The importance of conceptual variation and incommensurability. Research in Science Education, 43, 755–780.

  • Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12(3), 306–355.

    Google Scholar 

  • Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38.

    Google Scholar 

  • Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.

    Google Scholar 

  • Gupta, A., Hammer, D., & Redish, E. F. (2010). The case for dynamic models of learners’ ontologies in physics. The Journal of the Learning Sciences, 19(3), 285–321.

    Google Scholar 

  • Hammer, D., & Manz, E. (2019). Odd ideas about learning science: A response to Osborne. Science Education, 103(5), 1289–1293.

    Google Scholar 

  • Harlen, W. (Ed.). (2010). Principles and big ideas of science education. Association for Science Education.

  • 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.

    Google Scholar 

  • Hesse, M. (2008). Models and analogies. In W. H. Newton (Ed.), A companion to the philosophy of science (pp. 299–307). Blackwell Publishers Ltd.

    Google Scholar 

  • Kali, Y., Orion, N., & Eylon, B. S. (2003). Effect of knowledge integration activities on students’ perception of the Earth’s crust as a cyclic system. Journal of Research in Science Teaching, 40(6), 545–565.

    Google Scholar 

  • Kapon, S., & diSessa, A. A. (2012). Reasoning through instructional analogies. Cognition and Instruction, 30(3), 261–310.

    Google Scholar 

  • Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2(2), 285–305.

    Google Scholar 

  • Kuo, E., & Wieman, C. E. (2016). Toward instructional design principles: Inducing Faraday’s law with contrasting cases. Physical Review Physics Education Research, 12(1), 010128.

    Google Scholar 

  • Loughran, J. J., Berry, A., & Mulhall, P. (2006). Understanding and developing science teachers’ pedagogical content knowledge. Brill.

  • Lesh, R. A., & Doerr, H. M. (2003). Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. Routledge.

    Google Scholar 

  • Mäki, U. (2011). Models and the locus of their truth. Synthese, 180(1), 47–63.

    Google Scholar 

  • Martin, J. D., & Nock, K. A. (2018). A Nonlinear, “Sticky” Web of Study for Chemistry: A Graphical Curricular Tool for Teaching and Learning Chemistry Built upon the Interconnection of Core Chemical Principles. Journal of Chemical Education, 95(12), 2134–2140.

    Google Scholar 

  • Maton, K. (2013). Making semantic waves: A key to cumulative knowledge-building. Linguistics and Education, 24(1), 8–22.

    Google Scholar 

  • Mitchell, I., Keast, S., Panizzon, D., & Mitchell, J. (2017). Using ‘big ideas’ to enhance teaching and student learning. Teachers and Teaching, 23(5), 596–610.

    Google Scholar 

  • Mortimer, E., & Scott, P. (2003). Meaning Making In Secondary Science Classrooms. McGraw-Hill Education.

  • National Research Council. (2000). How people learn: Brain, mind, experience, and school: Expanded edition.

  • National Research Council. (2002). Learning and Understanding: Improving Advanced Study of Mathematics and Science in U.S. High Schools: Report of the Content Panel for Biology. The National Academies Press.

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.

  • NGSS Lead States. (2013). Next Generation Science Standards: For States, By States. The National Academies Press.

    Google Scholar 

  • Nordine, J., Fortus, D., Lehavi, Y., Neumann, K., & Krajcik, J. (2018). Modelling energy transfers between systems to support energy knowledge in use. Studies in Science Education, 54(2), 177–206.

    Google Scholar 

  • Novick, S. (1976). No energy storage in chemical bonds. Journal of Biological Education, 10(3), 116–118.

    Google Scholar 

  • Osborne, J. (2011). Science teaching methods: A rationale for practices. School Science Review, 93(343), 93–103.

    Google Scholar 

  • Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), 177–196.

    Google Scholar 

  • Patro, E. T. (2008). Teaching aerobic cell respiration using the 5 Es. The American Biology Teacher, 70(2), 85–87.

    Google Scholar 

  • Plummer, J. D., Palma, C., Rubin, K., Flarend, A., Ong, Y. S., Ghent, C., & Furman, T. (2020). Evaluating a learning progression for the solar system: Progress along gravity and dynamical properties dimensions. Science Education, 104(3), 530–554.

  • Redish, E. F., Bauer, C., Carleton, K. L., Cooke, T. J., Cooper, M., Crouch, C. H., & Klymkowsky, M. W. (2014). NEXUS/Physics: An interdisciplinary repurposing of physics for biologists. American Journal of Physics, 82(5), 368–377.

  • Redish, E. F., & Cooke, T. J. (2013). Learning each other’s ropes: negotiating interdisciplinary authenticity. CBE—Life Sciences Education, 12(2), 175–186.

  • Ross, P. M., Tronson, D. A., & Ritchie, R. J. (2008). Increasing conceptual understanding of glycolysis & the Krebs cycle using role-play. The American Biology Teacher, 70(3), 163–169.

    Google Scholar 

  • Russ, R. S., Scherr, R. E., Hammer, D., & Mikeska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Education, 92(3), 499–525.

    Google Scholar 

  • Scherr, R. E., Close, H. G., McKagan, S. B., & Vokos, S. (2012). Representing energy. I. Representing a substance ontology for energy. Physical Review Special Topics-Physics Education Research, 8(2), 020114.

  • Schmidt, W. H., McKnight, C. C., & Raizen, S. (Eds.). (2007). A splintered vision: An investigation of US science and mathematics education (Vol. 3). Springer Science & Business Media.

  • Schmidt, W. H., McKnight, C. C., Valverde, G., Houang, R. T., & Wiley, D. E. (Eds.). (1997). Many visions, many aims: A cross-national investigation of curricular intentions in school mathematics (Vol. 1). Springer Science & Business Media.

  • Scholer, A. M., & Hatton, M. (2008). An evaluation of the efficacy of a laboratory exercise on cellular respiration. Journal of College Science Teaching, 38(1), 40.

    Google Scholar 

  • Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759–775.

    Google Scholar 

  • Schwarz, C. V., Passmore, C., & Reiser, B. J. (2017). Helping students make sense of the world using next generation science and engineering practices. NSTA Press.

    Google Scholar 

  • Shemwell, J. T., Chase, C. C., & Schwartz, D. L. (2015). Seeking the general explanation: A test of inductive activities for learning and transfer. Journal of Research in Science Teaching, 52(1), 58–83.

    Google Scholar 

  • Sikorski, T. R., & Hammer, D. (2017). Looking for coherence in science curriculum. Science Education, 101(6), 929–943.

    Google Scholar 

  • Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). Implications of Research on Children’s Learning for Standards and Assessment: A Proposed Learning Progression for Matter and the Atomic-Molecular Theory. Measurement: Interdisciplinary Research and Perspectives, 4(1–2), 1–98.

  • Smith, J. P., III., & Girod, M. (2003). John Dewey & psychologizing the subject-matter: Big ideas, ambitious teaching, and teacher education. Teaching and Teacher Education, 19(3), 295–307.

    Google Scholar 

  • Songer, C. J., & Mintzes, J. J. (1994). Understanding cellular respiration: An analysis of conceptual change in college biology. Journal of Research in Science Teaching, 31(6), 621–637.

    Google Scholar 

  • Songer, N. B. (1989). Promoting integration of instructed and natural world knowledge in thermodynamics. University of California.

    Google Scholar 

  • Stevens, S. Y., Delgado, C., & Krajcik, J. S. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47(6), 687–715.

    Google Scholar 

  • Storey, R. D. (1992). Textbook errors & misconceptions in biology: Cell energetics. The American Biology Teacher, 54(3), 161–166.

    Google Scholar 

  • Stroupe, D. (2014). Examining classroom science practice communities: How teachers and students negotiate epistemic agency and learn science-as-practice. Science Education, 98(3), 487–516.

    Google Scholar 

  • Swackhamer, G. (2005). Cognitive resources for understanding energy. Retrieved January 10, 2022, from

  • Symeonidis, V., & Schwarz, J. F. (2016). Phenomenon-based teaching and learning through the pedagogical lenses of phenomenology: The recent curriculum reform in Finland. In Forum Oświatowe (Vol. 28, No. 2 (56), pp. 31–47). University of Lower Silesia.

  • Teichert, M. A., & Stacy, A. M. (2002). Promoting understanding of chemical bonding and spontaneity through student explanation and integration of ideas. Journal of Research in Science Teaching, 39(6), 464–496.

    Google Scholar 

  • Todd, A., & Kenyon, L. (2016). Empirical refinements of a molecular genetics learning progression: The molecular constructs. Journal of Research in Science Teaching, 53(9), 1385–1418.

    Google Scholar 

  • Wiggins, G., & McTighe, J. (2005).Understanding by design. (Expanded 2nd ed.). ASCD.

  • White, P. J. (2016). Molecular sculpting: Active learning of subcellular systems & processes. The American Biology Teacher, 78(6), 482–491.

    Google Scholar 

  • Windschitl, M., Thompson, J., & Braaten, M. (2020). Ambitious science teaching. Harvard Education Press.

  • Windschitl, M., Thompson, J., Braaten, M., & Stroupe, D. (2012). Proposing a core set of instructional practices and tools for teachers of science. Science Education, 96(5), 878–903.

    Google Scholar 

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We wish to acknowledge team members Eric Kirk who helped with the initial development of instructional materials and Michael Elgin Leary who supported in data collection and analysis. We are also grateful for the commentary of two anonymous reviewers who helped us understand our work more clearly.


This material is based on research supported by the National Science Foundation under Grant Numbers 1720996, 2010334, and 2010223.

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Correspondence to Daniel K. Capps.

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This research was conducted under the approval of Institutional Review Boards at The University of Alabama (IRB #17-OR-415-R1 and IRB #20–08-328) and the University of Georgia (IRB #00004700 and IRB #00002425).

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Shemwell, J.T., Capps, D.K., Fackler, A.K. et al. Integrative Analysis Using Big Ideas: Energy Transfer and Cellular Respiration. J Sci Educ Technol 32, 510–529 (2023).

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