Research in Science Education

, Volume 48, Issue 1, pp 71–103 | Cite as

Preservice Biology Teachers’ Conceptions About the Tentative Nature of Theories and Models in Biology

  • Bianca Reinisch
  • Dirk Krüger


In research on the nature of science, there is a need to investigate the role and status of different scientific knowledge forms. Theories and models are two of the most important knowledge forms within biology and are the focus of this study. During interviews, preservice biology teachers (N = 10) were asked about their understanding of theories and models. They were requested to give reasons why they see theories and models as either tentative or certain constructs. Their conceptions were then compared to philosophers’ positions (e.g., Popper, Giere). A category system was developed from the qualitative content analysis of the interviews. These categories include 16 conceptions for theories (n tentative = 11; n certain  = 5) and 18 conceptions for models (n tentative = 10; n certain = 8). The analysis of the interviews showed that the preservice teachers gave reasons for the tentativeness or certainty of theories and models either due to their understanding of the terms or due to their understanding of the generation or evaluation of theories and models. Therefore, a variety of different terminology, from different sources, should be used in learning-teaching situations. Additionally, an understanding of which processes lead to the generation, evaluation, and refinement or rejection of theories and models should be discussed with preservice teachers. Within philosophy of science, there has been a shift from theories to models. This should be transferred to educational contexts by firstly highlighting the role of models and also their connections to theories.


Nature of science Scientific theory Model Preservice teachers Conceptions Interviews 


  1. Adúriz-Bravo, A. (2012). A ‘semantic’ view of scientific models for science education. Science & Education, 22, 1593–1611.CrossRefGoogle Scholar
  2. Bacon, F. (1620/1990). Neues Organon, Teilband I. Herausgegeben und mit einer Einleitung von Wolfgang Krohn. Lateinisch - Deutsch [Novum Organum, Volume I. Edited from and with an introduction of Wolfgang Krohn. Latin - German]. Hamburg: Felix Meiner.Google Scholar
  3. Bailer-Jones, D. M. (1999). Tracing the development of models in the philosophy of science. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 23–40). Boston: Springer.Google Scholar
  4. Bailer-Jones, D. M. (2002). Naturwissenschaftliche Modelle: von Epistemologie zu Ontologie [Scientific models: from epistemology to ontology]. In A. Beckermann & C. Nimtz (Eds.), Argument und analyse. Sektionsvorträge (pp. 1–11). Paderborn: Mentis.Google Scholar
  5. Bailer-Jones, D. M. (2004). Realist-Sein im Blick auf naturwissenschaftliche Modelle [Being a realist with regard to scientific models]. In C. Halbig & C. Suhm (Eds.), Was ist wirklich? Neuere Beiträge zu Realismusdebatten in der Philosophie (pp. 201–221). Frankfurt: Ontos.Google Scholar
  6. Bailer-Jones, D. M. (2009). Scientific models in philosophy of science. Pittsburgh, PA: University of Pittsburgh.CrossRefGoogle Scholar
  7. Bailer-Jones, D. M., & Hartmann, S. (1999). Modell [Model]. In H.-J. Sandkühler (Ed.), Enzyklopädie Philosophie (pp. 854–859). Hamburg: Felix Meiner.Google Scholar
  8. Bell, R. L. (2009). Teaching the nature of science: three critical questions. Best Practices in Science Education, 15. Retrieved from
  9. Bortz, J., & Döring, N. (2006). Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler [Research methods and evaluation for social scientists]. Heidelberg: Springer.Google Scholar
  10. Bybee, R. W. (2002). Scientific literacy - Mythos oder Realität? [Scientific literacy - myth or reality?]. In W. Gräber, P. Nentwig, T. Koballa, & R. Evans (Eds.), Scientific literacy (pp. 21–43). Wiesbaden: Verlag für Sozialwissenschaften.Google Scholar
  11. Carnap, R. (1939). Foundations of logic and mathematics. Chicago, IL: University of Chicago.Google Scholar
  12. Chinn, C. A., & Brewer, W. F. (1998). An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching, 35, 623–654.CrossRefGoogle Scholar
  13. Clough, M. (2011). The story behind the science: bringing science and scientists to life in post-secondary science education. Science & Education, 20, 701–717.CrossRefGoogle Scholar
  14. Dagher, Z. R., Brickhouse, N. W., Shipman, H., & Letts, W. J. (2004). How some college students represent their understandings of the nature of scientific theories. International Journal of Science Education, 26, 735–755.CrossRefGoogle Scholar
  15. Dagher, Z. R., & Erduran, S. (2014). Laws and explanations in biology and chemistry: philosophical perspectives and educational implications. In M. R. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 1203–1233). Dordrecht: Springer.Google Scholar
  16. Duit, R., Gropengießer, H., Kattmann, U., Komorek, M., & Parchmann, I. (2012). The model of educational reconstruction - a framework for improving teaching and learning science. In D. Jorde & J. Dillon (Eds.), Science education research and practice in Europe (pp. 13–37). Rotterdam: Sense.Google Scholar
  17. Elby, A., & Hammer, D. (2001). On the substance of a sophisticated epistemology. Science Education, 85, 554–567.CrossRefGoogle Scholar
  18. Erduran, S. (2014). Beyond nature of science: the case for reconceptualising ‘science’ for science education. Science Education International, 25, 93–111.Google Scholar
  19. Erduran, S., & Dagher, Z. R. (2014). Reconceptualizing the nature of science for science education. Scientific knowledge, practices and other family categories. Dordrecht: Springer.Google Scholar
  20. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: verbal reports as data. Cambridge: MIT.Google Scholar
  21. Giere, R. N. (1988). Explaining science. A cognitive approach. Chicago, IL: University of Chicago.CrossRefGoogle Scholar
  22. Giere, R. N. (1999). Using models to represent reality. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery. Proceedings of an international conference on model-based reasoning in scientific discovery, held December 17–19, 1998, in Pavia, Italy (pp. 41–57). New York: Kluwer Academic/Plenum.Google Scholar
  23. Giere, R. N. (2001). A new framework for teaching scientific reasoning. Argumentation, 15, 21–33.CrossRefGoogle Scholar
  24. Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71, 42–752.CrossRefGoogle Scholar
  25. Giere, R. N., Bickle, J., & Mauldin, R. F. (2006). Understanding scientific reasoning. Belmont: Thomson/Wadsworth.Google Scholar
  26. Gropengießer, H. (2001). Didaktische Rekonstruktion des Sehens. In Wissenschaftliche Theorien und die Sicht der Schüler in der Perspektive der Vermittlung [Educational reconstruction of the process of seing, Scientific theories and the view of students under the perspective of teaching]. Oldenburg: Didaktisches Zentrum.Google Scholar
  27. Gropengießer, H. (2010). Qualitative Inhaltsanalyse in der fachdidaktischen Lehr-Lernforschung [Qualitative content analysis within subject-didactic teaching and learning research]. In P. Mayring & M. Gläser-Zikuda (Eds.), Die Praxis der qualitativen Inhaltsanalyse (pp. 172–189). Weinheim: Beltz.Google Scholar
  28. Grosslight, L., Unger, C., Jay, E., & Smith, C. L. (1991). Understanding models and their use in science: conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28, 799–822.CrossRefGoogle Scholar
  29. Grossman, P. L. (1990). The making of a teacher: teacher knowledge and teacher education. New York: Teachers College.Google Scholar
  30. Hodson, D. (2014). Learning science, learning about science, doing science: different goals demand different learning methods. International Journal of Science Education, 36, 2534–2553.CrossRefGoogle Scholar
  31. Hodson, D., & Wong, S. L. (2014). From the horse’s mouth: why scientists’ views are crucial to nature of science understanding. International Journal of Science Education, 36, 2639–2665.CrossRefGoogle Scholar
  32. Hoyningen-Huene, P. (2013). Systematicity: the nature of science. New York: Oxford University.CrossRefGoogle Scholar
  33. Irzik, G., & Nola, R. (2011). A family resemblance approach to the nature of science for science education. Science & Education, 20, 591–607.CrossRefGoogle Scholar
  34. Irzik, G., & Nola, R. (2014). New directions for nature of science research. In M. R. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 999–1021). Dordrecht: Springer.Google Scholar
  35. Justi, R., & Gilbert, J. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25, 1369–1386.CrossRefGoogle Scholar
  36. Kattmann, U., Duit, R., Gropengießer, H., & Komorek, H. (1997). Das Modell der Didaktischen Rekonstruktion - Ein Rahmen für naturwissenschaftsdidaktische Forschung und Entwicklung [The model of educational reconstruction - a framework for science education research and development]. Zeitschrift für Didaktik der Naturwissenschaften, 3, 3–18.Google Scholar
  37. KMK [Sekretariat der Ständigen Konferenz der Kultusminister der Länder in der BRD] (2005). Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss. [Biology education standards for the Mittlere Schulabschluss]. München: Wolters Kluwer Deutschland. Retrieved from Scholar
  38. KMK (2014). Ländergemeinsame inhaltliche Anforderungen für die Fachwissenschaften und Fachdidaktiken in der Lehrerbildung [Common requirements with regards to content for subject disciplines and didactics within teacher education]. Berlin: Author. Retrieved from Scholar
  39. Kohlhauf, L., Rutke, U., & Neuhaus, B. (2011). Influence of previous knowledge, language skills and domain-specific interest on observation competency. Journal of Science Education and Technology, 20, 667–678.CrossRefGoogle Scholar
  40. Krell, M., & Krüger, D. (2016). Testing models: A key aspect to promote teaching activities related to models and modelling in biology lessons? Journal of Biological Education, 50, 160–173.Google Scholar
  41. Krell, M., Reinisch, B., & Krüger, D. (2015). Analyzing students’ understanding of models and modeling referring to the disciplines biology, chemistry, and physics. Research in Science Education, 45, 367–393.Google Scholar
  42. Kuhn, T. (1962/2012). The structure of scientific revolutions. Chicago, IL: University of Chicago.Google Scholar
  43. Lakatos, I. (1977/1982). Die Methodologie der wissenschaftlichen Forschungsprogramme [The methodology of scientific research programmes]. Braunschweig: Friedr. Vieweg & Sohn.Google Scholar
  44. Lauth, B., & Sareiter, J. (2005). Wissenschaftliche Erkenntnis. Eine ideengeschichtliche Einführung in die Wissenschaftstheorie [Scientific cognition. Introduction in science philosophy in terms of the history of ideas]. Paderborn: Mentis.Google Scholar
  45. Lederman, N. G. (1992). Students’ and teachers’ conceptions of the nature of science: a review of the research. Journal of Research in Science Teaching, 29, 331–359.CrossRefGoogle Scholar
  46. Lederman, N. G. (2007). Nature of science: past, present, and future. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 831–880). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  47. Lederman, N. G., Abd-el-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire. Toward valid and meaningful assessment of learners’ conceptions of nature of scienc. Journal of Research in Science Teaching, 39, 497–521.CrossRefGoogle Scholar
  48. Lederman, N. G., & Lederman, J. S. (2014). Research on teaching and learning of nature of science. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education, volume II (pp. 600–620). New York: Routledge.Google Scholar
  49. Liang, L. L., Chen, S., Chen, X., Kaya, O. N., Adams, A. D., Macklin, M., & Ebenezer, J. (2009). Preservice teachers’ views about nature of scientific knowledge development: an international collaborative study. International Journal of Science and Mathematics Education, 7, 987–1012.CrossRefGoogle Scholar
  50. Mahr, B. (2012). On the epistemology of models. In G. Abel & J. Conant (Eds.), Rethinking epistemology (pp. 301–352). Berlin: Walter de Gruyter.Google Scholar
  51. Matthews, M. R. (2012). Changing the focus: from nature of science (NOS) to features of science (FOS). In M. S. Khine (Ed.), Advances in nature of science research. Concepts and methodologies (pp. 3–26). Dordrecht: Springer.CrossRefGoogle Scholar
  52. Mayring, P. (2000). Qualitative inhaltsanalyse [Qualitative content analysis]. Forum Qualitative Sozialforschung, 1, 28 para. Retrieved from
  53. Mayring, P. (2002). Qualitative content analysis - research instrument or mode of interpretation? In M. Kiegelmann (Ed.), The role of the researcher in qualitative psychology (pp. 139–148). Tübingen: Ingeborg Huber.Google Scholar
  54. McClure, J. R., Sonak, B., & Suen, H. K. (1999). Concept map assessment of classroom learning: reliability, validity, and logistical practicality. Journal of Research in Science Teaching, 36, 475–492.CrossRefGoogle Scholar
  55. McComas, W. F. (2002). The principal elements of the nature of science: dispelling the myths. In W. F. McComas (Ed.), The nature of science in science education. Rationales and strategies (pp. 53–70). Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  56. McComas, W. F., & Olson, J. K. (2002). The nature of science in international science education standards documents. In W. F. McComas (Ed.), The nature of science in science education. Rationales and strategies (pp. 41–52). Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
  57. Mikelskis-Seifert, S., & Fischler, H. (2003). Die Bedeutung des Denkens in Modellen bei der Entwicklung von Teilchenvorstellungen - Stand der Forschung und Entwurf einer Unterrichtskonzeption [On the role of thinking in terms of models when developing particle ideas – State of research and a draft of an instructional approach]. Zeitschrift für Didaktik der Naturwissenschaften, 9, 75–88.Google Scholar
  58. NGSS Lead States (2013). Next generation science standards: for states, by states. Washington, DC: National Academy.Google Scholar
  59. Niebert, K., & Gropengießer, H. (2014). Leitfadengestützte Interviews [Guideline based interviews]. In D. Krüger, I. Parchmann, & H. Schecker (Eds.), Methoden in der naturwissenschaftsdidaktischen Forschung (pp. 121–132). Berlin: Springer.CrossRefGoogle Scholar
  60. Novak, J. D., & Cañas, A. J. (2006). The theory underlying concept maps and how to construct them: technical Report IHMC CmapTools 2006–01. Retrieved from
  61. Nuzzo, A. (1999). Theorie [Theory]. In H. J. Sandkühler, D. Pätzold, A. Regenbogen, & P. Stekeler-Weithofer (Eds.), Enzyklopädie Philosophie (1620b-1624b). Hamburg: Felix Meiner.Google Scholar
  62. Osborne, J., Collins, S., Ratcliffe, M., Millar, R., & Duschl, R. (2003). What “ideas-about-science” should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40, 692–720.CrossRefGoogle Scholar
  63. Passmore, C., Svoboda Gouvea, J., & Giere, R. N. (2014). Models in science and in learning science: focusing scientific practice on sense-making. In M. R. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 1171–1202). Dordrecht: Springer.Google Scholar
  64. Popper, K. R. (1934/1971). Logik der Forschung [The logic of scientific discovery]. Tübingen: J. C. B. Mohr.Google Scholar
  65. Reichenbach, H. (1930a). Die philosophische Bedeutung der modernen Physik [The philosophical meaning of modern physics]. Erkenntnis, 1, 49–71.CrossRefGoogle Scholar
  66. Reichenbach, H. (1930b). Kausalität und Wahrscheinlichkeit [Causality and probability]. Erkenntnis, 1, 158–188.CrossRefGoogle Scholar
  67. Reinisch, B., & Krüger, D. (2014). Vorstellungen von Studierenden über Gesetze, Theorien und Modelle in der Biologie [Conceptions of pre-service teachers about laws, theories, and models in biology]. Erkenntnisweg Biologiedidaktik, 13, 41–56.Google Scholar
  68. Reutlinger, A., Schurz, G., & Hüttemann, A. (2014). Ceteris Paribus laws. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Stanford: Stanford University. Retrieved from Scholar
  69. Rosenberg, A. (2008). Biology. In S. Psillos & M. Curd (Eds.), Routledge philosophy companions. The Routledge companion to philosophy of science (pp. 511–519). London: Routledge.Google Scholar
  70. Samarapungavan, A., Westby, E. L., & Bodner, G. M. (2006). Contextual epistemic development in science: a comparison of chemistry students and research chemists. Science Education, 90, 468–495.CrossRefGoogle Scholar
  71. Sandoval, W. A. (2005). Understanding students’ practical epistemologies and their influence on learning through inquiry. Science Education, 89, 634–656.CrossRefGoogle Scholar
  72. Schatzman, L., & Strauss, A. L. (1973). Field research. Strategies for a natural sociology. Englewood Cliffs: Prentice-Hall.Google Scholar
  73. Schmidt, C. (2010). Auswertungstechniken für Leitfadeninterviews [Analysing technique for guided interviews]. In B. Friebertshäuser, A. Langer, & A. Prengel (Eds.), Handbuch qualitative Forschungsmethoden in der Erziehungswissenschaft (pp. 473–486). Weinheim: Juventa.Google Scholar
  74. Schreier, M. (2014). Varianten qualitativer Inhaltsanalyse: Ein Wegweiser im Dickicht der Begrifflichkeiten [Ways of doing qualitative content analysis: disentangling terms and terminologies]. Forum Qualitative Sozialforschung, 15, 59 para. Retrieved from
  75. Schwartz, R. S., Lederman, N. G., & Abd-el-Khalick, F. (2012). A series of misrepresentations: a response to Allchin’s whole approach to assessing nature of science understandings. Science Education, 96, 685–692.CrossRefGoogle Scholar
  76. Shulman, L. S. (1986). Those who understand: knowledge growth in teaching. Educational Researcher, 15, 4–14.CrossRefGoogle Scholar
  77. Shulman, L. S. (1987). Knowledge and teaching: foundations of the new reform. Harvard Educational Review, 57, 1–22.CrossRefGoogle Scholar
  78. Tracy, S. (2013). Qualitative research methods: collecting evidence, crafting analysis, communicating impact. Chichester: Wiley-Blackwell.Google Scholar
  79. van Dijk, E. M. (2013). Paul Hoyningen-Huene: systematicity: the nature of science. Science & Education, 22, 2369–2373.CrossRefGoogle Scholar
  80. van Dijk, E. M. (2014). Understanding the heterogeneous nature of science: a comprehensive notion of PCK for scientific literacy. Science Education, 98, 397–411.CrossRefGoogle Scholar
  81. Vollmer, G. (1975a). Evolutionäre erkenntnistheorie: angeborene erkenntnisstrukturen im kontext von biologie, psychologie, linguistik, philosophie und wissenschaftstheorie [Evolutionary theory of knowledge: innate cognition structures in the context of biology, psychology, linguistic, philosophy, and philosophy of science]. Stuttgart: S. Hirzel.Google Scholar
  82. Vollmer, G. (1975b). Was können wir wissen? Die Erkenntnis der Natur. [What can we know? The cognition of nature]. Stuttgart: S. Hirzel.Google Scholar
  83. Vosniadou, S., Vamvakoussi, X., & Skopeliti, I. (2008). The framework theory approach to the problem of conceptual change. In S. Vosniadou (Ed.), Educational psychology handbook series. International handbook of research on conceptual change (pp. 3–34). New York: Routledge.Google Scholar
  84. Wiltsche, H. A. (2013). Einführung in die Wissenschaftstheorie [Introduction to the philosophy of science]. Göttingen: Vandenhoeck & Ruprecht.Google Scholar
  85. Windschitl, M., Thompson, J., & Braaten, M. (2008). How novice science teachers appropriate epistemic discourses around model-based inquiry for use in classrooms. Cognition and Instruction, 26, 310–378.CrossRefGoogle Scholar
  86. Wong, S. L., & Hodson, D. (2009). From the horse’s mouth: what scientists say about scientific investigation and scientific knowledge. Science Education, 93, 109–130.CrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Freie Universität BerlinBerlinGermany

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