Skip to main content

A Framework for Modeling-Based Learning, Teaching, and Assessment

  • Chapter
  • First Online:
Towards a Competence-Based View on Models and Modeling in Science Education

Abstract

The modeling-based learning framework is an approach to science learning involving model construction, refinement, and validation. We begin by describing the epistemological underpinnings and the rationale for a modeling-based teaching and learning approach for developing knowledge of natural phenomena. We proceed by describing the modeling-based learning framework in terms of modeling practices (model construction, model use, model revision, model comparison, and model validation) and the modeling of meta-knowledge (knowledge about models and metacognitive knowledge of the modeling process) that emerge as one develops expertise in scientific modeling. We also present a process for identifying levels of attainment for each component of the framework and examples of such attainment levels. Our core argument refers to the interconnectedness of the practical and epistemological aspects of modeling-based learning and the usefulness of the framework for designing teaching-learning sequences and assessments. We compare and contrast the modeling-based learning framework with the basic features of the framework for modeling competence, and we discuss the implementation of the modeling-based learning framework into meaningful learning and teaching practices.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It is important to clarify the idea that scientific models are different from mental models (Gentner & Stevens, 1983), which are Cognitive Psychology constructs that refer to “transient representations that are activated usually when one is exposed to a new situation and act as structural analogies to situations or processes” (Greca & Moreira, 2002, p. 108).

  2. 2.

    These researchers propose that meta-modeling knowledge consists of (i) the nature of models, (ii) the nature or process of modeing, (iii) the evaluation of models, and (iv) the purpose or utility of models.

References

  • Berland, L. K., & Reiser, B. J. (2009). Making sense of argumentation and explanation. Science Education, 93(1), 26–55. https://doi.org/10.1002/sce.20286

    Article  Google Scholar 

  • Braaten, M., & Windschitl, M. (2011). Working toward a stronger conceptualization of scientific explanation for science education. Science Education, 95(4), 639–669. https://doi.org/10.1002/sce.20449

    Article  Google Scholar 

  • Bunge, M. (1983). Treatise on basic philosophy: Volume 5: Epistemology & methodology I: Exploring the world. Dordrecht, The Netherlands: Springer/Reidel.

    Google Scholar 

  • Chapman, O. L. (2000). Learning science involves language, experience and modeling. Journal of Applied Developmental Psychology, 21(1), 97–108.

    Article  Google Scholar 

  • Cheng, M.-F., & Lin, J.-L. (2015). Investigating the relationship between students’ views of scientific models and their development of models. International Journal of Science Education, 0693(September), 1–23. https://doi.org/10.1080/09500693.2015.1082671

    Article  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.

    Article  Google Scholar 

  • Constantinou, C. P. (1999). The cocoa microworld as an environment for developing modeling skills in physical science. International Journal of Continuing Education and Life-Long Learning, 9(2), 201–213.

    Article  Google Scholar 

  • Constantinou, C. P., & Papadouris, N. (2004). Potential contribution of digital video to the analysis of the learning process in physics: A case study in the context of electric circuits. Educational Research and Evaluation, 10(1), 21–39. https://doi.org/10.1076/edre.10.1.21.26300

    Article  Google Scholar 

  • Constantinou, C. P., & Papadouris, N. (2012). Teaching and learning about energy in middle school: An argument for an epistemic approach. Studies in Science Education, 48(2), 161–186. https://doi.org/10.1080/03057267.2012.726528

    Article  Google Scholar 

  • Crawford, B., & Cullin, M. (2004). Supporting prospective teachers’ conceptions of modelling in science. International Journal of Science Education, 26(11), 1379–1401.

    Article  Google Scholar 

  • De Jong, O., Van Driel, J. H., & Verloop, N. (2005). Preservice teachers’ pedagogical content knowledge of using particle models in teaching chemistry. Journal of Research in Science Teaching, 42(8), 947–964. https://doi.org/10.1002/tea.20078

    Article  Google Scholar 

  • Dori, Y., & Kaberman, Z. (2012). Assessing high school chemistry students’ modeling sub-skills in a computerized molecular modeling learning environment. Instructional Science, 40(1), 69–91. https://doi.org/10.1007/s11251-011-9172-7

    Article  Google Scholar 

  • Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academy Press.

    Google Scholar 

  • Fretz, E. B., Wu, K., Zhang, B., Davis, E., Krajcik, J., & Soloway, E. (2002). An invstigation of software scaffolds supporting modelling practices. Reseach in Science Education, 32, 567–589.

    Article  Google Scholar 

  • Giere, R. N. (1999). Using models to represent reality. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 41–57). New York: Kluwer/Plenum.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Gilbert, J., Boulter, C., & Rutherford, M. (1998). Models in explanations, part 2: Whose voice, whose ears? International Journal of Science Education, 20(2), 187–203.

    Article  Google Scholar 

  • Gilbert, S. (1991). Model building and a definition of science. Journal of Research in Science Teaching, 28(1), 73–79.

    Article  Google Scholar 

  • Gobert, J. D., & Buckley, C. B. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22(9), 891–894.

    Article  Google Scholar 

  • Gobert, J. D., O’Dwyer, L., Horwitz, P., Buckley, B. C., Levy, S. T., & Wilensky, U. (2011). Examining the relationship between students’ understanding of the nature of models and conceptual learning in biology, physics, and chemistry. International Journal of Science Education, 33(5), 653–684. https://doi.org/10.1080/09500691003720671

    Article  Google Scholar 

  • Grünkorn, J., zu Belzen, A. U., & Krüger, D. (2014). Assessing students’ understandings of biological models and their use in science to evaluate a theoretical framework. International Journal of Science Education, 36(10), 1651–1684. https://doi.org/10.1080/09500693.2013.873155

    Article  Google Scholar 

  • Guisasola, J., Almudí, J. M., & Zubimendi, J. L. (2004). Difficulties in learning the introductory magnetic field theory in the first years of university. Science Education, 88(3), 443–464. https://doi.org/10.1002/sce.10119

    Article  Google Scholar 

  • Halloun, I. (1996). Schematic modeling for meaningful learning of physics. Journal of Research in Science Teaching, 33, 1019–1041.

    Article  Google Scholar 

  • Hokayem, H., & Schwarz, C. (2014). Engaging fifth graders in scientific modeling to learn about evaporation and condensation. International Journal of Science and Mathematics Education, 12(1), 49–72. https://doi.org/10.1007/s10763-012-9395-3.

    Article  Google Scholar 

  • Hughes, R. (1997). Models and representation. Philosophy of Science, 64, S325–S336.

    Google Scholar 

  • Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2012). Students’ understanding of the purpose of models in different biological contexts. International Journal of Biology Education, 2(2), 1–34.

    Google Scholar 

  • 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(3), 367–393. https://doi.org/10.1007/s11165-014-9427-9

    Article  Google Scholar 

  • Louca, L., Zacharia, Z., & Constantinou, C. P. (2011). In quest of productive modeling-based learning discourse in elementary school science. Journal of Research in Science Teaching, 48(8), 919–951. https://doi.org/10.1002/tea.20435

    Article  Google Scholar 

  • Machamer, P., Darden, L., & Carver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(67), 1–25. https://doi.org/10.1086/392759

    Article  Google Scholar 

  • Mahr, B. (2009) Information science and the logic of models. Software and Systems Modeling, 8:365–383.

    Google Scholar 

  • Maia, P. F., & Justi, R. (2009). Learning of chemical equilibrium through modelling-based teaching. International Journal of Science Education, 31(5), 603–630.

    Article  Google Scholar 

  • Marton, F. (1981). Phenomenography-describing conceptions of the world around us. Instructional Science, 10, 177–200.

    Article  Google Scholar 

  • Mendonça, P. C. C., & Justi, R. (2014). An instrument for analyzing arguments produced in modeling-based chemistry lessons. Journal of Research in Science Teaching, 51(2), 192–218. https://doi.org/10.1002/tea.21133

    Article  Google Scholar 

  • Namdar, B., & Shen, J. (2015). Modeling-oriented assessment in K-12 science education: A synthesis of research from 1980 to 2013 and new directions. International Journal of Science Education, 37(7), 993–1023. https://doi.org/10.1080/09500693.2015.1012185

    Article  Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press. http://www.nap.edu/catalog.php?record_id=13165

    Google Scholar 

  • National Research Council. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press. doi.org/https://doi.org/https://doi.org/10.17226/18290.

  • Nersessian, N. J. (2008). Model-based reasoning in scientific practice. In R. A. Duschl & R. E. Grandy (Eds.), Teaching scientific inquiry: Recommendations for research and implementation (pp. 57–79). Sense Publishers.

    Google Scholar 

  • Nicolaou, C. T. (2010). Modelling and collaboration in learning environments (in Greek). Department of Education. Doctoral dissertation. University of Cyprus Nicosia, Cyprus (ISBN: 978-9963-689-84-2).

    Google Scholar 

  • Nicolaou, C. T., & Constantinou, C. P. (2014). Assessment of the modeling competence: A systematic review and synthesis of empirical research. Educational Research Review, 13(3), 52–73.

    Article  Google Scholar 

  • Nicolaou, C. T., Nicolaidou, I. A., & Constantinou, C. P. (2009). Scientific model construction by pre-service teachers using Stagecast creator. In P. Blumstein, W. Hung, D. Jonassen, & J. Strobel (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. 215–236). Rotterdam, The Netherlands: Sense Publishers.

    Google Scholar 

  • OECD. (2003). The definition and selection of key competencies – Executive summary. DeSeCo, 1–20. https://doi.org/10.1080/2159676X.2012.712997

  • Papadouris, N., & Constantinou, C. P. (2014). An exploratory investigation of 12-year-old students’ ability to appreciate certain aspects of the nature of science through a specially designed approach in the context of energy. International Journal of Science Education, 36(5), 755–782. https://doi.org/10.1080/09500693.2013.827816

    Article  Google Scholar 

  • Papaevripidou, M. (2012). Teachers as learners and curriculum designers in the context of modeling-centered scientific inquiry. Doctoral dissertation, Learning in Science Group, Department of Educational Sciences, University of Cyprus, Nicosia, Cyprus (ISBN: 978-9963-700-56-1).

    Google Scholar 

  • Papaevripidou, M., Constantinou, C. P., & Zacharia, Z. C. (2007). Modeling complex marine ecosystems: An investigation of two teaching approaches with fifth graders. Journal of Computer Assisted Learning, 23(2), 145–157. https://doi.org/10.1111/j.1365-2729.2006.00217.x

    Article  Google Scholar 

  • Papaevripidou, M., Nicolaou, C. T., & Constantinou, C. P. (2014). On defining and assessing learners’ modeling competence in science teaching and learning. In Annual Meeting of American Educational Research Association (AERA), Philadelphia, Pennsylvania, USA.

    Google Scholar 

  • Passmore, C., & Stewart, J. (2002). A modeling approach to teaching evolutionary biology in high schools. Journal of Research in Science Teaching, 39(3), 185–204.

    Article  Google Scholar 

  • Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34(2), 125–143.

    Article  Google Scholar 

  • Pluta, W. J., Chinn, C. A., & Duncan, R. G. (2011). Learners’ epistemic criteria for good scientific models. Journal of Research in Science Teaching, 48(5), 486–511. https://doi.org/10.1002/tea.20415

    Article  Google Scholar 

  • Raftopoulos, A., Kalyfommatou, N., & Constantinou, C. P. (2005). The properties and the nature of light: The study of Newton’s work and the teaching of optics. Science & Education, 14(7–8), 649–673. https://doi.org/10.1007/s11191-004-5609-6

    Article  Google Scholar 

  • Rychen, D. S., & Salganik, L. H. (2003). Key competencies for a successful life and a well-functioning society.

    Google Scholar 

  • Sandoval, W. A. (2015). Epistemic goals (and scientific reasoning). Encyclopedia of Science Education, (January 2015), 694698. https://doi.org/10.1007/978-94-007-2150-0

  • Schwartz, R. S., & Lederman, N. G. (2008). What scientists say: Scientists’ views of nature of science and relation to science context. International Journal of Science Education, 30(6), 727–771. https://doi.org/10.1080/09500690701225801

    Article  Google Scholar 

  • 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(4), 685–692. https://doi.org/10.1002/sce.21013

    Article  Google Scholar 

  • Schwarz, C., Reiser, B. J., Acher, A., Kenyon, L., & Fortus, D. (2012). MoDeLS: Challenges in defining a learning progression for scientific modeling. In A. C. Alonso & A. W. Gotwals (Eds.), Learning progressions in science: Current challenges and future directions (pp. 101–137). Rotterdam, The Netherlands: Sense Publishers.

    Chapter  Google Scholar 

  • Schwarz, C., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., et al. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. https://doi.org/10.1002/tea.20311

    Article  Google Scholar 

  • Schwarz, C., & White, B. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.

    Article  Google Scholar 

  • Sins, P. H. M., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The difficult process of scientific modelling: An analysis of novices’ reasoning during computer-based modelling. International Journal of Science Education, 27(14), 1695–1721.

    Article  Google Scholar 

  • Stratford, S. J., Krajcik, J., & Soloway, E. (1998). Secondary students’ dynamic modeling processes: Analyzing, reasoning about, synthesizing, and testing models of stream ecosystems. Journal of Science Education and Technology, 7(3), 215–234.

    Article  Google Scholar 

  • Upmeier zu Belzen, A., & Krüger, D. (2010). Modellkompetenz im Biologieunterricht [Model competence in biology classes]. Zeitschrift Für Didaktik Der Naturwissenschaften, 16, 41–57. http://archiv.ipn.uni-kiel.de/zfdn/pdf/16_Upmeier.pdf

    Google Scholar 

  • van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212.

    Article  Google Scholar 

  • van der Valk, T., van Driel, J. H., & de Vos, W. (2007). Common characteristics of models in present-day scientific practice. Research in Science Education, 37(4), 469–488. https://doi.org/10.1007/s11165-006-9036-3.

  • Van Lehn, K. (2013). Model construction as a learning activity: A design space and review. Interactive Learning Environments, 21(4), 371–413. https://doi.org/10.1080/10494820.2013.803125

    Article  Google Scholar 

  • Weinert, F. E. (2001). Concept of competence: A conceptual clarification. In Definition and selection of competencies: Theoretical and conceptual foundation (DeSeCo) (pp. 44–65). https://doi.org/10.1073/pnas.0703993104

  • White, B. Y. and Frederiksen. J. R. (1990) Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42(1), 99–157.

    Google Scholar 

  • Wu, H. K., Krajcik, J., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821–842.

    Article  Google Scholar 

  • Xiang, L., & Passmore, C. (2015). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, 24(2–3), 311–329. https://doi.org/10.1007/s10956-014-9534-4

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Constantinos P. Constantinou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Constantinou, C.P., Nicolaou, C.T., Papaevripidou, M. (2019). A Framework for Modeling-Based Learning, Teaching, and Assessment. In: Upmeier zu Belzen, A., Krüger, D., van Driel, J. (eds) Towards a Competence-Based View on Models and Modeling in Science Education. Models and Modeling in Science Education, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-30255-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30255-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30254-2

  • Online ISBN: 978-3-030-30255-9

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics