Abstract
The goal of this study was to identify and understand the mental models developed by 67 high school biology students as they learn about the human body as a complex system. Using concept maps, it sought to find an external way of representing how students organize their ideas about the human body system in their minds. We conducted a qualitative analysis of four concept maps created by each student throughout the 3-year learning process, which allowed us to identify that student’s systems thinking skills and the development of those skills over time. The improvement trajectories of the students were defined according to three central characteristics of complex systems: (a) hierarchy, (b) homeostasis and (c) dynamism. A comparative analysis of all of our students’ individual trajectories together revealed four typical learning patterns, each of which reflects a different form of development for systems thinking: “from the structure to the process level”, “from macro to micro level”, “from the cellular level to the organism level,” and “development in complexity of homeostasis mechanisms”. Despite their differences, each of these models developed over time from simpler structures, which evolved as they connected with more complex system aspects, and each indicates advancement in the student’s systems thinking.
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Alonzo, A. C., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93, 389–421. https://doi.org/10.1002/sce.20303.
Ausubel, D. P. (1968). Educational psychology: A cognitive view. New York: Holt, Rinehart & Winston.
Ausubel, D., Novak, J., & Hanesian, H. (1978). Educational psychology: A cognitive view (2nd ed.). New York: Rinehart & Winston.
Ben-Zvi Assaraf, O., Dodick. J., & Tripto, J. (2013). High school students' understanding of the Human Body System. Research in Science Education, 43(1), 33–56.
Ben-Zvi Assaraf, O., & Orion, N. (2005). Development of system thinking skills in the context of Earth System education. Journal of Research in Science Teaching, 42(5), 518–560.
Ben-Zvi Assaraf, O., & Orion, N. (2010). Four case studies, six years later: Developing system thinking skills in junior high school and sustaining them over time. Journal of Research in Science Teaching, 47(10), 1253–1280.
Boersma, K., Waarlo, A. J., & Klaassen, K. (2011). The feasibility of systems thinking in biology education. Journal of Biological Education, 45(4), 190–197.
Bray-Speth, E., Shaw, N., Momsen, J., Reinagel, A., Le, P., Taqieddin, R., et al. (2014). Introductory biology students’ conceptual models and explanations of the origin of variation. CBE-Life Sciences Education, 13(3), 529–539.
Buckley, B. C., & Boulter, C. J. (2000). Investigating the role of representations and expressed models in building mental models. In J. K. Gilbert & C. Boulter (Eds.), Developing models in science education (pp. 105–122). Dordrecht: Kluwer.
Chang, S. N. (2007). Externalising students’ mental models through concept maps. Journal of Biological Education, 41(3), 107–112. https://doi.org/10.1080/00219266.2007.9656078.
Chang, S. N., & Chiu, M. H. (2004). Probing students’ conceptions concerning homeostasis of blood sugar via concept mapping. In Proceedings of the annual meeting of the national association for Research in Science Teaching (pp. 1–4). Vancouver/Canada.
Chase, S. E. (2005). Narrative inquiry: Multiple lenses, approaches, voices. In N. K. Denzin & Y. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 651–679). Thousand Oaks, CA: Sage.
Chi, M. T. H., De Leew, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.
Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. San Francisco: Jossey-Bass.
Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. CA: Sage.
Dauer, J. T., & Long, T. M. (2015). Long-term conceptual retrieval by college biology majors following model-based instruction. Journal of Research in Science Teaching, 52, 1188–1206. https://doi.org/10.1002/tea.21258.
Dauer, J. T., Momsen, J. L., Speth, E. B., Makohon-Moore, S. C., & Long, T. M. (2013). Analyzing change in students’ gene-to-evolution models in college-level introductory biology. Journal of Research in Science Teaching, 5(6), 639–659.
Duncan, R. G., & Reiser, B. J. (2007). Reasoning across ontologically distinct levels: Students’ understandings of molecular genetics. Journal of Research in Science Teaching, 44(7), 938–959. https://doi.org/10.1002/tea.20186.
Evagorou, M., Korfiatis, K., Nicolaou, C., & Constantinou, C. (2009). An investigation of the potential of interactive simulations for developing system thinking skills in elementary school: A case study with fifth-graders and sixth-graders. International Journal of Science Education, 31(5), 655–674. https://doi.org/10.1080/09500690701749313.
Goel, A., Rugaber, S., & Vattam, S. (2009). Structure, behavior & function of complex systems: The SBF modeling language. International Journal of AI in Engineering Design, Analysis and Manufacturing, 23, 23–35. https://doi.org/10.1017/S0890060409000080.
Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. Journal of the Learning Sciences, 17(4), 465–516. https://doi.org/10.1080/10508400802394898.
Hay, D. B. (2007). Using concept maps to measure deep, surface and non-learning outcomes. Studies in Higher Education, 32(1), 39–57.
Hay, D. B., Kinchin, I. M., & Lygo-Baker, S. (2008). Making learning visible: The role of concept mapping in higher education. Studies in Higher Education., 33(3), 295–311.
Henige, K. (2012). Use of concept mapping in an undergraduate introductory exercisephysiology course. Advances in Physiology Education, 36(3), 197–206. https://doi.org/10.1152/advan.00001.2012.
Hmelo-Silver, C. E., & Azevedo, R. A. (2006). Understanding complex systems: Some core challenges. Journal of the Learning Sciences, 15, 53–61.
Hmelo-Silver, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing learning about complex systems. Journal of the learning Science, 9(3), 247–298. https://doi.org/10.1207/S15327809JLS0903_2.
Hmelo-Silver, C. E., Jordan, R., Eberbach, C., & Goel, A. (2011). Systems and cycles: Learning about aquatic ecosystems. Society for Research on Educational Effectiveness. Resource document http://files.eric.ed.gov/fulltext/ED528796.pdf
Hmelo-Silver, C. E., Jordan, R., Eberbach, C., & Sinha, S. (2017). Systems learning with a conceptual representation: a quasi-experimental study. Instructional Science, 45(1), 53–72. https://doi.org/10.1007/s11251-016-9392-y.
Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16(3), 307–331.
Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.
Hung, P. H., Hwang, G. J., Su, I. H., & I-Hua, L. (2012). A concept-map integrated dynamic assessment system for improving ecology observation competences in mobile learning activities. The Turkish Online. Journal of Educational Technology (TOJET), 11(1), 10–19.
Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.
Ifenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how we can detect it: Tracking the development of cognitive structures over time. International Science, 39, 41–61. https://doi.org/10.1007/s11251-009-9097-6.
Israeli Ministry of Education. (2015). Curriculum in biology in high school (10th–12th grades). State of Israel Ministry of Education Curriculum Center (2015). Retrieved from: http://cms.education.gov.il/EducationCMS/Units/Mazkirut_Pedagogit/Biology/TochnitLimudim/tochnitmutemet.htm
Jacobson, M. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.
Jacobson, M. J., Markauskaite, L., Portolese, A., Kapur, M., Lai, P. K., & Roberts, G. (2017). Designs for learning about climate change as a complex system. Learning and Instruction.
Jacobson, M., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34.
Johnson-Laird, P. N. (2001). Mental models and deduction. TRENDS in Cognitive Sciences, 5(10), 434–442.
Johnson-Laird, P. N. (2004). The history of mental models. In K. Manktelow & M. C. Chung (Eds.), Psychology of reasoning: Theoretical and historical perspectives (pp. 179–212). New York: Psychology Press.
Jonassen, D., Beissner, K., & Yacci, M. (2013). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Routledge.
Jordan, R. C., Hmelo-Silver, C., Liu, L., & Gray, S. A. (2013). Fostering reasoning about complex systems: Using the aquarium to teach systems thinking. Applied Environmental Education & Communication, 12, 55–64. https://doi.org/10.1080/1533015X.2013.797860.
Kalinowski, S. T., Leonard, M. J., & Andrews, T. M. (2010). Nothing in evolution makes sense except in the light of DNA. CBE-Life Sciences Education, 9(2), 87–97.
Kinchin, I. M. (2001). Can a novice be viewed as an expert upside-down? School Science Review, 303(83), 91–95.
Kinchin, I. M. (2011). Visualising knowledge structures in biology: Discipline, curriculum and student understanding. Journal of Biological Education, 45(4), 183–189. https://doi.org/10.1080/00219266.2011.598178.
Kinchin, I. M., Hay, D. B., & Adams, A. (2000). How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development. Educational Research, 42(1), 43–57. https://doi.org/10.1080/001318800363908.
Knippels, M. C. P. J. (2002). Coping with the abstract and complex nature of genetics in biology education: The yo–yo teaching and learning Strategy. PhD Dissertation, Proefschrift Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/219
Lin, C.-Y., & Hu, R. (2003). Students’ understanding of energy flow and matter cycling in the context of the food chain, photosynthesis, and respiration. Journal of Science Education, 25(12), 1529–1544. https://doi.org/10.1080/0950069032000052045.
Liu, L., & Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 46(9), 1023–1040. https://doi.org/10.1002/tea.20297.
Long, T. M., Dauer, J. T., Kostelnik, K. M., Momsen, J. L., Wyse, S. A., Speth, E. B., et al. (2014). Fostering ecoliteracy through model-based instruction. Frontiers in Ecology and the Environment, 12(2), 138–139.
Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83(4), 484–490.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.
Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: John Wiley & Sons.
Mintzes, J. J., Wandersee, J. H., & Novak, J. D. (2000). Assessing science understanding: A human constructivist view. San Diego: Academic Press.
National Research Council. (2007). Taking science to school. Washington, DC: The National Academies Press.
National Research Council. (2010). Exploring the intersection of science education and 21st century skills: A workshop summary. National Academy Press.
Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448.
NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington DC: National Academy Press.
Novak, J. D. (1990). Concept maps and vee diagrams: Two metacognitive tools for science and mathematics education. Instructional Science, 19, 29–52.
Novak, J. D. (1993). How do we learn our lesson? Taking students throug the process. The Science Teacher, 3(60), 51–55.
Novak, J. D., & Canas, A. J. (2007). Theoretical origins of concept maps, how to construct them, and uses in education. Reflecting Education, 3(1), 29–42.
Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge University Press. https://doi.org/10.1017/CBO9781139173469.
Plate, R. (2010). Assessing individuals’ understanding of nonlinear causal structures in complex systems. System Dynamics Review, 26(1), 19–33.
Raved, L., & Yarden, A. (2014). Developing seventh grade students’ systems thinking skills in the context of the human circulatory system. Frontiers in Public Health, 2, 60. https://doi.org/10.3389/fpubh.2014.00260.
Reiner, M., & Eilam, B. (2001). Conceptual classroom environment-a system view of learning. International Journal of Science Education, 23(6), 551–568.
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awarness; contemporary. Educational Psychology, 19, 460–475.
Schroeder, N. L., Nesbit, J. C., Anguiano, C. J., & Adesope, O. O. (2017). Studying and constructing concept maps: A meta-analysis. Educational Psychology Review. https://doi.org/10.1007/s10648-017-9403-9.
Shavelson, R. J., Ruiz-Primo, M. A., & Wiley, E. W. (2005). Windows into the mind. Higher Education, 49(4), 413–430.
Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model. Dordrecht: Springer.
Sommer, C., & Lücken, M. (2010). System competence—Are elementary students able to deal with a biological system? NorDiNa—Nordic Studies in Science Education, 6(2), 125–143. Resource document http://www.naturfagsenteret.no/c1515603/binfil/download2.php?tid=1568379
Tripto, J., Ben-Zvi Assaraf, O., Snapir, Z., & Amit, M. (2016). A Reflection Interview - "What is a system" as a knowledge integration activity for high school students' understanding of complex systems in human biology. International Journal of Science Education, 38(4), 564–595.
Tripto, J., Ben-Zvi Assaraf, O., Snapir, Z., & Amit, M. (2017). How does the body’s systemic nature manifested amongst high school biology students? Instructional Science: Special Issue Proposal Models and Tools for Systems Learning and Instruction, 45, 73–98.
Tsui, C.-Y., & Treagust, D. F. (2013). Introduction to multiple representations: Their importance in Biology and Biological Education. In D. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biological education (p. 7). New York: Springer.
Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., et al. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Educational Technology & Society, 14(1), 66–81.
Verhoeff, R. P., Waarlo, A. J., & Boersma, K. T. (2008). Systems modelling and the development of coherent understanding of cell biology. International Journal of Science Education, 30(4), 543–568. https://doi.org/10.1080/09500690701237780.
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.
Wilson, C. D., Anderson, C. W., Heidemann, M., Merrill, J. E., Merritt, B. W., Richmond, G., et al. (2006). Assessing students’ ability to trace matter in dynamic systems in cell biology. Life Science Education., 5, 323–331. https://doi.org/10.1187/cbe.06-02-0142.
Wu, P. H., Hwang, G. J., Milrad, M., Ke, H. R., & Huang, Y. M. (2012). An innovative concept map approach for improving students’ learning performance with an instant feedback mechanism. Journal of Educational Technology, 43(2), 217–232.
Yoon, S. A., Anderson, E., Koehler-Yom, J., Evans, C., Park, M., Sheldon, J., et al. (2016). Teaching about complex systems is no simple matter: Building effective professional development for computer-supported complex systems instruction. Instructional Science, 45(1), 99–121. https://doi.org/10.1007/s11251-016-9388-7.
Zion, M., & Klein, S. (2015). Conceptual understanding of homeostasis. International Journal of Biology Education, 4(1), 1–27.
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This research was supported by the ISRAELI SCIENCE FOUNDATION Research Grant Application no. 718/11.
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Appendix
Correlation between Concept Maps and the STH Model
Below is a step-by-step description of how concept maps can be read as indicators of system thinking, based on the correlation of their contents to the STH model. The description is divided according to the model’s three basic levels, and further subdivided into the model’s eight individual characteristics.
Level A: Analysis of System Components
Characteristic #1: Identifying components and processes in the human body system. Characterizing system thinking at the components and processes level requires the following steps:
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(1)
Selecting a suitable characteristic into which all the concepts written by the student may be pooled. In this study we chose hierarchy in nature.
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(2)
Dividing this master-characteristic into the categories—Structure and Process
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(3)
Further dividing each of these into the sub-categories of Microscopic and Macroscopic levels.
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(4)
Sorting the concepts written by the students into each of the categories now present under the master-characteristic hierarchy in nature.
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(5)
Counting all of the concepts provided by the student to arrive at an overall number of concepts.
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(6)
Counting the number of concepts in each category.
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(7)
Calculating distributions for the estimation of the student’s relative ability to represent system components vs. system processes.
For a more thorough insight into the student’s treatment of components vs. processes, the maps should also be analyzed according to the connections the student has made between the concepts. This necessitates the following:
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(a)
Counting all the connections made by the student. A connection is a word describing a connection between two concepts. For instance: The veins transfer blood from the heart to the body. The italicizes words represent the connections drawn between the (underlined) concepts.
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(b)
Analyzing the contents of the connections to derive statements. “Veins transfer blood from the heart to the body”.
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(c)
Sorting the resulting statements and removing those that are irrelevant to the study topic.
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(d)
Sorting the statements into process/non-process related. A process-related statement refers to a string of actions or changes that are assigned a certain order within a gradual development. On the other hand, a merely descriptive statement would refer statically to an object’s state or appearance.
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(e)
Calculating distributions to compare process/non-process oriented statements.
Level B: Synthesis of System Components
Characteristic #2: Identifying simple relationships between system components. Evidence in concept maps of relationships between system components can be gathered by identifying both the concepts in a student’s body of knowledge, and the manner of their organization into meaningful connections. To do this one must:
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(a)
Analyze the connections and translate them into statements.
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(b)
Identify statements that address relationships between components (i.e. statements that address the effect of element ‘x’ upon element “y”).
Characteristic #3: Identifying dynamic relationships in systems. This ability can be measured by the examination of the connection a student has formed between two concepts. To do this one must:
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(a)
Analyze connections and translate them into statements.
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(b)
Identify statements that express dynamism (i.e. statements in which the student refers to the transmission of a certain substance within the human body system).
Characteristic #4: Organizing components and processes within a framework of relationships. Students’ ability to connect a single component to a large number of other components can be assessed by examining the number of junctions on their concept map. A “junction” is a concept that has connections to at least three other concepts on the map. The number of junctions students mark between their concepts provides insight into the level of knowledge integration they have undergone. For this reason, the junctions in each map should be counted.
Level C: Implementation
Characteristic #6: Generalization and identification of patterns. Concept maps allow us to identify students’ understanding of patterns in human body systems by analyzing the contents of their connections. To do this, the statements derived from these connections must be sorted, and those statements that relate to patterns identified. The three patterns to be looked for are: Homeostasis, Hierarchy and Dynamism. Homeostasis includes statements that generally describe the body’s internal stability (“the concentration of urea and water in the body is regulated by homeostasis”). Hierarchy includes statements referring to scale in nature, while emphasizing one scale in relation to another (“the circulatory system includes capillaries”). Dynamism includes statements that address dynamic processes as system characteristics that occur in the human body (“oxygen enters the body through the lungs”).
Characteristic #7: Identifying hidden dimensions. To assess this characteristic, the statements derived from the map must be sorted, and those that refer to internal patterns and connections that are invisible on the body’s surface must be identified.
Characteristic #8: Temporal thinking. This includes both retrospective thinking (backwards) and projection (forwards). To identify a student’s understanding that interactions taking place in the present can bring about and influence future events, those statements from the map in which there are temporal references must be identified.
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Tripto, J., Assaraf, O.B.Z. & Amit, M. Recurring patterns in the development of high school biology students’ system thinking over time. Instr Sci 46, 639–680 (2018). https://doi.org/10.1007/s11251-018-9447-3
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DOI: https://doi.org/10.1007/s11251-018-9447-3