Skip to main content

Assessing Modeling Competence with Questionnaires

  • Chapter
  • First Online:

Part of the book series: Models and Modeling in Science Education ((MMSE,volume 12))

Abstract

Students’ as well as pre-service teachers’ and in-service teachers’ modeling competence is an important issue of research in science education due to its influence on both assessment and teaching. A large number of studies have used different methodological approaches, ranging from interviews to closed-ended tasks. In this chapter, we aim to provide an overview of the studies that have employed either open-ended tasks or closed-ended tasks as a way to elicit students’, pre-service teachers’, and in-service teachers’ understanding of models and modeling. We present different assessment instruments that contain, for example, multiple-choice, forced-choice, or rating scale tasks and summarize the main findings of studies on these instruments. As a second step, the results across these studies are compared, and, based on current standards for educational testing, the advantages and limitations of each of the instruments regarding the purpose of assessing and diagnosing perspectives on models and modeling in science are discussed. Bringing all aspects together, a variety of approaches for task and test development are illustrated, including concepts with regard to validation methods in particular.

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

Buying options

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

Learn about institutional subscriptions

References

  • AERA, APA, & NCME (Eds.). (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Al-Balushi, S. M. (2011). Students’ evaluation of the credibility of scientific models that represent natural entities and phenomena. International Journal of Science and Mathematics Education, 9, 571–601. https://doi.org/10.1007/s10763-010-9209-4

    Article  Google Scholar 

  • Bamberger, Y. M., & Davis, E. A. (2013). Middle-school science students’ scientific modelling performances across content areas and within a learning progression. International Journal of Science Education, 35, 213–238. https://doi.org/10.1080/09500693.2011.624133

    Article  Google Scholar 

  • Borrmann, J. R., Reinhardt, N., Krell, M., & Krüger, D. (2014). Perspektiven von Lehrkräften über Modelle in den Naturwissenschaften: Eine generalisierende Replikationsstudie [Teachers perspectives’ on models in science: A generalizing replication study]. Erkenntnisweg Biologiedidaktik, 13, 57–72.

    Google Scholar 

  • Campbell, T., Oh, P. S., Maughn, M., Kiriazis, N., & Zuwallack, R. (2015). A review of modeling pedagogies: pedagogical functions, discursive acts, and technology in modeling instruction. EURASIA Journal of Mathematics, Science & Technology Education, 11, 159-176. https://doi.org/10.12973/eurasia.2015.1314a

    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, 37, 2453–2475. https://doi.org/10.1080/09500693.2015.1082671

    Article  Google Scholar 

  • Chittleborough, G. D., Treagust, D. F., Mamiala, T. L., & Mocerino, M. (2005). Students’ perceptions of the role of models in the process of science and in the process of learning. Research in Science & Technological Education, 23, 195–212. https://doi.org/10.1080/02635140500266484

    Article  Google Scholar 

  • Crawford, B. A., & Cullin, M. J. (2004). Supporting prospective teachers’ conceptions of modelling in science. International Journal of Science Education, 26, 1379–1401. https://doi.org/10.1080/09500690410001673775

  • Crawford, B. A., & Cullin, M. J. (2005). Dynamic assessments of preservice teachers’ knowledge of models and modelling. In K. Boersma, M. Goedhart, O. de Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 309–323). Dordrecht, The Netherlands, Springer. https://doi.org/10.1007/1-4020-3673-6_25

  • Danusso, L., Testa, I., & Vicentini, M. (2010). Improving prospective teachers’ knowledge about scientific models and modelling: Design and evaluation of a teacher education intervention. International Journal of Science Education, 32, 871–905. https://doi.org/10.1080/09500690902833221

    Article  Google Scholar 

  • Derman, A., & Kayacan, K. (2017). Investigation of the relationship between the views of the prospective science teachers on the nature of scientific models and their achievement on the topic of atom. European Journal of Education Studies, 3, 541–559.

    Google Scholar 

  • Everett, S. A., Otto, C. A., & Luera, G. R. (2009). Preservice elementary teachers’ growth in knowledge of models in a science capstone course. International Journal of Science and Mathematics Education, 7, 1201–1225. https://doi.org/10.1007/s10763-009-9158-y

    Article  Google Scholar 

  • Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education. Cham, Switzerland: Springer.

    Book  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, 653–684. https://doi.org/10.1080/09500691003720671

  • Gogolin, S. (2017). Diagnosing students’ meta-modelling knowledge: Gathering validity evidence during test development. Doctoral dissertation. Freie Universität, Berlin, Germany. Retrieved from http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000105919

  • Gogolin, S., Krell, M., Lange-Schubert, K., Hartinger, A., Upmeier Zu Belzen, A., & Krüger, D. (2017). Erfassung von Modellkompetenz bei Grundschüler/innen [Assessment of elementary students’ modeling competence]. In H. Giest, A. Hartinger, & S. Tänzer (Eds.), Vielperspektivität im Sachunterricht (pp. 108–115). Bad Heilbrunn, Germany: Klinkhardt.

    Google Scholar 

  • 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. https://doi.org/10.1002/tea.3660280907

    Article  Google Scholar 

  • Grünkorn, J., Upmeier zu Belzen, A., & 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, 1651–1684. https://doi.org/10.1080/09500693.2013.873155

    Article  Google Scholar 

  • Justi, R., & Gilbert, J. K. (2005). Investigating teachers’ ideas about models and modelling: Some issues of authenticity. In K. Boersma, M. Goedhart, O. de Jong, & H. Eijkelhof (Eds.), Research and the quality of science education (pp. 325–335). Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Justi, R., & van Driel, J. (2005). The development of science teachers’ knowledge on models and modelling: Promoting, characterizing, and understanding the process. International Journal of Science Education, 27, 549–573. https://doi.org/10.1080/0950069042000323773

    Article  Google Scholar 

  • Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50, 1–73. https://doi.org/10.1111/jedm.2013.50.issue-1

  • Kane, M. (2015). Validation strategies. Delineating and validating proposed interpretations and uses of test scores. In M. Raymond, S. Lane, & T. Haladyna (Eds.), Handbook of Test Development (pp. 64-80). New York: Routledge.

    Google Scholar 

  • KMK (Ed.). (2005). Bildungsstandards im Fach Biologie für den Mittleren Schulabschluss [Biology education standards for the Mittlere Schulabschluss]. München, Neuwied: Wolters Kluwer.

    Google Scholar 

  • Krell, M. (2012). Using polytomous IRT models to evaluate theoretical levels of understanding models and modeling in biology education. Science Education Review Letters. Theoretical Letters 2012, 1–5.

    Google Scholar 

  • 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. https://doi.org/10.1080/00219266.2015.1028570

    Article  Google Scholar 

  • Krell, M., & Krüger, D. (2017). University students’ meta-modelling knowledge. Research in Science & Technological Education, 35, 261–273. https://doi.org/10.1080/02635143.2016.1274724

    Article  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, 367–393. https://doi.org/10.1007/s11165-014-9427-9

    Article  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, 1–34. Retrieved from http://www.ijobed.com/2_2/Moritz-2012.pdf

    Google Scholar 

  • Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014a). Students’ levels of understanding models and modelling in biology: Global or aspect-dependent? Research in Science Education, 44, 109–132. https://doi.org/10.1007/s11165-013-9365-y

    Article  Google Scholar 

  • Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2014b). Context-specificities in students’ understanding of models and modelling: An issue of critical importance for both assessment and teaching. In C. Constantinou, N. Papadouris, & A. Hadjigeorgiou (Eds.), E-book proceedings of the ESERA 2013 conference: Science education research for evidence-based teaching and coherence in learning (pp. 1024–1035). Nicosia, Cyprus: European Science Education Research Association. Retrieved from https://www.esera.org/publications/esera-conference-proceedings/esera-2013

    Google Scholar 

  • Krell, M., Upmeier zu Belzen, A., & Krüger, D. (2016). Modellkompetenz im Biologieunterricht [Modeling competence in biology education]. In A. Sandmann, & P. Schmiemann (Eds.), Biologiedidaktische Forschung: Schwerpunkte und Forschungsstände (pp. 83–102). Berlin, Germany: Logos.

    Google Scholar 

  • Lee, S. W.-Y. (2017). Identifying the item hierarchy and charting the progression across grade levels: Surveying Taiwanese students’ understanding of scientific models and modeling. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-017-9854-y

  • Lee, S. W.-Y., Chang, H.-Y., & Wu, H.-K. (2017). Students’ views of scientific models and modeling: Do representational characteristics of models and students’ educational levels matter? Research in Science Education, 47, 305–328. https://doi.org/10.1007/s11165-015-9502-x

    Article  Google Scholar 

  • Leighton, J. P. (2004). Avoiding misconception, misuse, and missed opportunities. The collection of verbal reports in educational achievement testing. Educational Measurement: Issues and Practice, 23, 6–15. https://doi.org/10.1111/j.1745-3992.2004.tb00164.x

    Article  Google Scholar 

  • Lin, J.-W. (2014). Elementary school teachers’ knowledge of model functions and modeling processes: A comparison of science and non-science majors. International Journal of Science and Mathematics Education, 12, 1197–1220. https://doi.org/10.1007/s10763-013-9446-4

    Article  Google Scholar 

  • Martinez, M. E. (1999). Cognition and the question of test item format. Educational Psychologist, 34, 207–218.

    Article  Google Scholar 

  • NGSS Lead States (Eds.). (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.

    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, 52–73. https://doi.org/10.1016/j.edurev.2014.10.001

    Google Scholar 

  • Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models. An overview. International Journal of Science Education, 33, 1109–1130. https://doi.org/10.1080/09500693.2010.502191

    Article  Google Scholar 

  • Osborne, J. (2013). The 21st century challenge for science education: Assessing scientific reasoning. Thinking Skills and Creativity, 10, 265–279. https://doi.org/10.1016/j.tsc.2013.07.006

    Article  Google Scholar 

  • Patzke, C., Krüger, D., & Upmeier zu Belzen, A. (2015). Entwicklung von Modellkompetenz im Längsschnitt [Development of modeling competence in a longitudinal study]. In M. Hammann, J. Mayer, & N. Wellnitz (Eds.), Lehr- und Lernforschung in der Biologiedidaktik: Vol. 6. Theorie, Empirie & Praxis: Internationale Tagung der Fachsektion Didaktik der Biologie im VBIO, Kassel 2013 (pp. 43–58). Innsbruck, Austria: Studienverlag.

    Google Scholar 

  • Pintó, R., & Gutierrez, R. (2005). Teachers’ conceptions of scientific models. In R. Pinto & D. Couso (Eds.), Proceedings of the 5th international ESERA conference on contributions of research to enhancing students’ interest in learning science (pp. 866–868). Barcelona, Spain: ESERA.

    Google Scholar 

  • Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23, 165–205. https://doi.org/10.1207/s1532690xci2302_1

    Article  Google Scholar 

  • Shavelson, R. J. (2013). On an approach to testing and modeling competence. Educational Psychologist, 48, 73–86. https://doi.org/10.1080/00461520.2013.779483

    Article  Google Scholar 

  • Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout Wolters, B. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modelling task. International Journal of Science Education, 31, 1205–1229. https://doi.org/10.1080/09500690802192181.

  • Terzer, E. (2013). Modellkompetenz im Kontext Biologieunterricht: Empirische Beschreibung von Modellkompetenz mithilfe von Multiple-Choice Items [Modeling competence in the context of biology education]. Doctoral dissertation. Humboldt-Universität, Berlin, Germany. Retrieved from: http://edoc.hu-berlin.de/dissertationen/terzer-eva-2012-12-19/PDF/terzer.pdf

  • Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2002). Students’ understanding of the role of scientific models in learning science. International Journal of Science Education, 24, 357–368. https://doi.org/10.1080/09500690110066485

    Article  Google Scholar 

  • Treagust, D. F., Chittleborough, G., & Mamiala, T. L. (2004). Students’ understanding of the descriptive and predictive nature of teaching models in organic chemistry. Research in Science Education, 34, 1–20. https://doi.org/10.1023/B:RISE.0000020885.41497.ed

  • Upmeier zu Belzen, A., & Krüger, D. (2010). Modellkompetenz im Biologieunterricht [Model competence in biology education]. Zeitschrift für Didaktik der Naturwissenschaften, 16, 41–57.

    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, 469–488. https://doi.org/10.1007/s11165-006-9036-3

    Article  Google Scholar 

  • Van Driel, J. H., & Verloop, N. (1999). Teachers’ knowledge of models and modelling in science. International Journal of Science Education, 21, 1141–1153. https://doi.org/10.1080/095006999290110

    Article  Google Scholar 

  • VCAA. (2016). Victorian certificate of education. Biology. Melbourne, VIC: VCAA.

    Google Scholar 

  • Wei, S., Liu, X., & Jia, Y. (2014). Using Rasch measurement to validate the instrument of students’ understanding of models in science (SUMS). International Journal of Science and Mathematics Education, 12, 1067–1082. https://doi.org/10.1007/s10763-013-9459-z

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sabrina Mathesius .

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

Mathesius, S., Krell, M. (2019). Assessing Modeling Competence with Questionnaires. 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_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30255-9_7

  • 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