Recurring patterns in the development of high school biology students’ system thinking over time

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 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.

    Article  Google Scholar 

  2. Ausubel, D. P. (1968). Educational psychology: A cognitive view. New York: Holt, Rinehart & Winston.

    Google Scholar 

  3. Ausubel, D., Novak, J., & Hanesian, H. (1978). Educational psychology: A cognitive view (2nd ed.). New York: Rinehart & Winston.

    Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. Boersma, K., Waarlo, A. J., & Klaassen, K. (2011). The feasibility of systems thinking in biology education. Journal of Biological Education, 45(4), 190–197.

    Article  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. 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.

  10. 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.

    Article  Google Scholar 

  11. 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.

  12. 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.

    Google Scholar 

  13. Chi, M. T. H., De Leew, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.

    Google Scholar 

  14. Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. San Francisco: Jossey-Bass.

    Google Scholar 

  15. Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. CA: Sage.

    Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. Hay, D. B. (2007). Using concept maps to measure deep, surface and non-learning outcomes. Studies in Higher Education, 32(1), 39–57.

    Article  Google Scholar 

  23. 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.

    Article  Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. Hmelo-Silver, C. E., & Azevedo, R. A. (2006). Understanding complex systems: Some core challenges. Journal of the Learning Sciences, 15, 53–61.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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

  28. 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.

    Article  Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. 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.

  32. Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.

    Article  Google Scholar 

  33. 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.

    Google Scholar 

  34. 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

  35. Jacobson, M. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.

    Article  Google Scholar 

  36. 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.

  37. 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.

    Article  Google Scholar 

  38. Johnson-Laird, P. N. (2001). Mental models and deduction. TRENDS in Cognitive Sciences, 5(10), 434–442.

    Article  Google Scholar 

  39. 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.

    Google Scholar 

  40. Jonassen, D., Beissner, K., & Yacci, M. (2013). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Routledge.

  41. 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.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. Kinchin, I. M. (2001). Can a novice be viewed as an expert upside-down? School Science Review, 303(83), 91–95.

    Google Scholar 

  44. 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.

    Article  Google Scholar 

  45. 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.

    Article  Google Scholar 

  46. Knippels, M. C. P. J. (2002). Coping with the abstract and complex nature of genetics in biology education: The yoyo teaching and learning Strategy. PhD Dissertation, Proefschrift Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/219

  47. 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.

    Article  Google Scholar 

  48. 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.

    Article  Google Scholar 

  49. 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.

    Article  Google Scholar 

  50. 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.

    Article  Google Scholar 

  51. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.

    Article  Google Scholar 

  52. Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: John Wiley & Sons.

    Google Scholar 

  53. Mintzes, J. J., Wandersee, J. H., & Novak, J. D. (2000). Assessing science understanding: A human constructivist view. San Diego: Academic Press.

    Google Scholar 

  54. National Research Council. (2007). Taking science to school. Washington, DC: The National Academies Press.

    Google Scholar 

  55. National Research Council. (2010). Exploring the intersection of science education and 21st century skills: A workshop summary. National Academy Press.

  56. Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448.

    Article  Google Scholar 

  57. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington DC: National Academy Press.

    Google Scholar 

  58. Novak, J. D. (1990). Concept maps and vee diagrams: Two metacognitive tools for science and mathematics education. Instructional Science, 19, 29–52.

    Article  Google Scholar 

  59. Novak, J. D. (1993). How do we learn our lesson? Taking students throug the process. The Science Teacher, 3(60), 51–55.

    Google Scholar 

  60. 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.

    Google Scholar 

  61. Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge University Press. https://doi.org/10.1017/CBO9781139173469.

    Google Scholar 

  62. Plate, R. (2010). Assessing individuals’ understanding of nonlinear causal structures in complex systems. System Dynamics Review, 26(1), 19–33.

    Article  Google Scholar 

  63. 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.

    Article  Google Scholar 

  64. Reiner, M., & Eilam, B. (2001). Conceptual classroom environment-a system view of learning. International Journal of Science Education, 23(6), 551–568.

    Article  Google Scholar 

  65. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awarness; contemporary. Educational Psychology, 19, 460–475.

    Google Scholar 

  66. 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.

    Google Scholar 

  67. Shavelson, R. J., Ruiz-Primo, M. A., & Wiley, E. W. (2005). Windows into the mind. Higher Education, 49(4), 413–430.

    Article  Google Scholar 

  68. Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K. M., Kauffman, D. F., & Herr, L. M. (2010). The unified learning model. Dordrecht: Springer.

    Google Scholar 

  69. Sommer, C., & Lücken, M. (2010). System competence—Are elementary students able to deal with a biological system? NorDiNaNordic Studies in Science Education, 6(2), 125–143. Resource document http://www.naturfagsenteret.no/c1515603/binfil/download2.php?tid=1568379

  70. 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.

    Article  Google Scholar 

  71. 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.

    Article  Google Scholar 

  72. 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.

    Google Scholar 

  73. 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.

    Google Scholar 

  74. 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.

    Article  Google Scholar 

  75. 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.

    Article  Google Scholar 

  76. 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.

    Article  Google Scholar 

  77. 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.

    Article  Google Scholar 

  78. 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.

    Article  Google Scholar 

  79. Zion, M., & Klein, S. (2015). Conceptual understanding of homeostasis. International Journal of Biology Education, 4(1), 1–27.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the ISRAELI SCIENCE FOUNDATION Research Grant Application no. 718/11.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Orit Ben Zvi Assaraf.

Appendix

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:

  1. (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.

  2. (2)

    Dividing this master-characteristic into the categories—Structure and Process

  3. (3)

    Further dividing each of these into the sub-categories of Microscopic and Macroscopic levels.

  4. (4)

    Sorting the concepts written by the students into each of the categories now present under the master-characteristic hierarchy in nature.

  5. (5)

    Counting all of the concepts provided by the student to arrive at an overall number of concepts.

  6. (6)

    Counting the number of concepts in each category.

  7. (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:

  1. (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.

  2. (b)

    Analyzing the contents of the connections to derive statements. “Veins transfer blood from the heart to the body”.

  3. (c)

    Sorting the resulting statements and removing those that are irrelevant to the study topic.

  4. (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.

  5. (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:

  1. (a)

    Analyze the connections and translate them into statements.

  2. (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:

  1. (a)

    Analyze connections and translate them into statements.

  2. (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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • High school biology
  • Complex systems
  • Systems thinking