Skip to main content
SpringerLink
Log in

Combining Visual and Verbal Data to Diagnose and Assess Modeling Competence

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

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

Abstract

The prominent role of verbal data in investigating modeling competence becomes obvious in the large number of studies that have used interviews and think-aloud methods to elicit conceptions about models and modeling, thus emphasizing the role of models as research tools. In this chapter, we discuss the characteristics, benefits, and drawbacks of these methods before presenting their broad application in modeling competence research. We furthermore discuss the role of visual data illuminating eye movements as a response to visual stimuli with respect to models and modeling. We thereby introduce eye-tracking technology as a way to expand insights in this field. In addition to providing an overview of how eye-tracking technology has been applied to date in science education, we suggest ways in which verbal and visual data can be triangulated to enrich perspectives on modeling competence.

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

Access this chapter

Log in via an institution
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.

    For detailed information about how to plan and conduct interviews, see, for example, Edwards and Holland (2013), Kvale and Brinkmann (2009), and Mason (2002).

  2. 2.

    Other methods of triangulation such as post-CTA interviews or recall have been discussed, for example, by Charters (2003).

  3. 3.

    For theory and details on the procedure of qualitative content analysis, please see Mayring (2014, 2016).

  4. 4.

    Ubben, Nitz, Daniel, and Upmeier zu Belzen (2018) proposed a detailed approach for how to assess levels of representational competence using the example of phylogenetic trees.

References

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Bojko, A. (2013). Eye tracking the user experience: A practical guide to research. Brooklyn, NY: Rosenfeld Media.

    Google Scholar 

  • Brinkmann, S. (2014). Interview. In Encyclopedia of critical psychology (pp. 1008–1010). New York: Springer.

    Chapter  Google Scholar 

  • Carey, S., Evans, R., Honda, M., Jay, E., & Unger, C. (1989). An experiment is when you try it and see if it works: A study of grade 7 students’ understanding of the construction of scientific knowledge. International Journal of Science Education, 11(5).

    Google Scholar 

  • Charters, E. (2003). The use of think-aloud methods in qualitative research: An introduction to think-aloud methods. Brock Education Journal, 12(2).

    Google Scholar 

  • Chen, S. C., She, H. C., Chuang, M. H., Wu, J. Y., Tsai, J. L., & Jung, T. P. (2014). Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities. Computers & Education, 74, 61–72.

    Article  Google Scholar 

  • Cheng, W. K. (2014). Relationship between visual attention and flow experience in a serious educational game: An eye tracking analysis. Doctoral dissertation, George Mason University.

    Google Scholar 

  • Chien, K. P., Tsai, C. Y., Chen, H. L., Chang, W. H., & Chen, S. (2015). Learning differences and eye fixation patterns in virtual and physical science laboratories. Computers & Education, 82, 191–201.

    Article  Google Scholar 

  • Crawford, B., & Cullin, M. (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.

    Chapter  Google Scholar 

  • Daniel, K.L. & Leone, E.A. (2017, June). Student visual interpretations of a cladogram. Poster presented at the AP Biology Posters on the Prairie symposium, Kansas City, MO, USA.

    Google Scholar 

  • Duchowski, A. (2017). Eye tracking methodology (3rd ed.). Cham, Switzerland: Springer.

    Book  Google Scholar 

  • Edwards, R., & Holland, J. (2013). What is qualitative interviewing? What is? Research methods series. London/New Delhi, India: Bloomsbury.

    Book  Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1993). Verbal reports as data. Psychological Review, 87(3), 215–251.

    Article  Google Scholar 

  • France, B., Compton, V. J., & Gilbert, J. K. (2011). Understanding modelling in technology and science: The potential of stories from the field. International Journal of Technology and Design Education, 21(3), 381–394.

    Article  Google Scholar 

  • Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523–552.

    Article  Google Scholar 

  • Gerrig, R. J., & Zimbardo, P. G. (2002). Psychology and life (16th ed.). Boston, MA: Allyn and Bacon.

    Google Scholar 

  • Gilbert, J., & Justi, R. (2016). Approaches to modelling-based teaching. In Modelling-based teaching in science education. Models and modeling in science education: Vol. 9. (pp. 57–80). Cham, Switzerland: Springer.

    Google Scholar 

  • Grosslight, L., Unger, C., & Jay, E. (1991). Understanding models and their use in science: Conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28(9), 799–822.

    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(10), 1651–1684.

    Article  Google Scholar 

  • Guan, Z., Lee, S., Cuddihy, E., & Ramey, J. (2006, April). The validity of the stimulated retrospective thinkaloud method as measured by eye tracking. In CHI ‘06: Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1253–1262). New York: ACM Press.

    Google Scholar 

  • Hannus, M., & Hyönä, J. (1999). Utilization of illustrations during learning of science textbook passages among low-and high-ability children. Contemporary Educational Psychology, 24(2), 95–123.

    Article  Google Scholar 

  • Harrison, A. G., & Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011–1026.

    Article  Google Scholar 

  • Hegarty, M., Mayer, R. E., & Green, C. E. (1992). Comprehension of arithmetic word problems: Evidence from students’ eye fixations. Journal of Educational Psychology, 84(1), 76.

    Article  Google Scholar 

  • Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford, UK/New York: Oxford University Press.

    Google Scholar 

  • Jarodzka, H., Scheiter, K., Gerjets, P., & van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20(2), 146–154.

    Article  Google Scholar 

  • Jarodzka, H., van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2013). Learning to see: Guiding students’ attention via a Model’s eye movements fosters learning. Learning and Instruction, 25, 62–70.

    Article  Google Scholar 

  • Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354.

    Article  Google Scholar 

  • Justi, R., & Gilbert, J. (2003). Teachers’ views on the nature of models. International Journal of Science Education, 25(11), 1369–1386.

    Article  Google Scholar 

  • Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369–387.

    Article  Google Scholar 

  • Kinner, V. L., Kuchinke, L., Dierolf, A. M., Merz, C. J., Otto, T., & Wolf, O. T. (2017). What our eyes tell us about feelings: Tracking pupillary responses during emotion regulation processes. Psychophysiology, 54(4), 508–518.

    Article  Google Scholar 

  • Kvale, S., & Brinkmann, S. (2009). Learning the craft of qualitative research interviewing. Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., et al. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.

    Article  Google Scholar 

  • Legrenzi, P., Girotto, V., & Johnson-Laird, P. N. (1993). Focussing in reasoning and decision making. Cognition, 49(1), 37–66.

    Article  Google Scholar 

  • Marshall, S. P. (2007). Identifying cognitive state from eye metrics. Aviation, Space, and Environmental Medicine, 78(5), 165–175.

    Google Scholar 

  • Mason, J. (2002). Qualitative researching (2nd ed.). London/Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Mason, L., Pluchino, P., Tornatora, M. C., & Ariasi, N. (2013). An eye-tracking study of learning from science text with concrete and abstract illustrations. The Journal of Experimental Education, 81(3), 356–384.

    Article  Google Scholar 

  • Mason, L., Tornatora, M. C., & Pluchino, P. (2013). Do fourth graders integrate text and picture in processing and learning from an illustrated science text? Evidence from eye-movement patterns. Computers & Education, 60, 95–109.

    Article  Google Scholar 

  • Mathesius, S., Upmeier zu Belzen, A., & Krüger, D. (2018). Eyetracking als Methode zur Untersuchung von Lösungsprozessen bei Multiple-Choice-Aufgaben zum wissenschaftlichen Denken [Eye tracking as a method for investigating the solving processes involved in multiple-choice tasks in scientific reasoning]. In M. Hammann & M. Lindner (Eds.), Lehr- und Lernforschung in der Biologiedidaktik. Innsbruck, Germany: Studienverlag.

    Google Scholar 

  • Mayring, P. (2014). Qualitative content analysis: theoretical foundation, basic procedures and software solution. Klagenfurt, Austria.

    Google Scholar 

  • Mayring, P. (2016). Einführung in die qualitative Sozialforschung: Eine Anleitung zu qualitativem Denken [Introduction to qualitative social research] (6th ed.). Weinheim, Germany/Basel, Switzerland: Beltz Verlag.

    Google Scholar 

  • Mele, M. L., & Federici, S. (2012). Gaze and eye-tracking solutions for psychological research. Cognitive Processing, 13(1), 261–265.

    Article  Google Scholar 

  • Miller, B. W. (2015). Using reading times and eye-movements to measure cognitive engagement. Educational Psychologist, 50(1), 31–42.

    Article  Google Scholar 

  • Morimoto, C. H., & Mimica, M. R. (2005). Eye gaze tracking techniques for interactive applications. Computer Vision and Image Understanding, 98(1), 4–24.

    Article  Google Scholar 

  • Novick, L. R., Stull, A. T., & Catley, K. M. (2012). Reading phylogenetic trees: The effects of tree orientation and text processing on comprehension. Bioscience, 62(8), 757–764.

    Article  Google Scholar 

  • Olson, G. M., Duffy, S. A., & Mack, R. L. (1984). Thinking-out-loud as a method for studying real-time comprehension processes. In D. E. Kieras & M. A. Just (Eds.), New methods in reading comprehension research (pp. 253–286). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Orsenne, J. (2015). Aktivierung von Schülervorstellungen zu Modellen durch praktische Tätigkeiten der Modellbildung [Activating students‘ conceptions of models with hands-on modeling tasks]. Dissertation, Humboldt-Universität zu Berlin, Berlin, Germany.

    Google Scholar 

  • Randhawa, B. (1994). Theory, research, and assessment of mathematical problem solving. The Alberta Journal of Educational Research, 40(2), 213–231.

    Google Scholar 

  • Rayner, K. (1998). Eye movements in reading and information processing. 20 years of research. Psychological Bulletin, 124(3), 372–422.

    Article  Google Scholar 

  • Rayner, K. (2009). The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search. Quarterly Journal of Experimental Psychology, 62(8), 1457–1506.

    Article  Google Scholar 

  • Rayner, K., Chace, K. H., Slattery, T. J., & Ashby, J. (2006). Eye movements as reflections of comprehension processes in reading. Scientific Studies of Reading, 10(3), 241–255.

    Article  Google Scholar 

  • Seidman, I. (2008). Interviewing as qualitative research: A guide for researchers in education and the social sciences (3rd ed.). New York/London: Teachers College Press.

    Google Scholar 

  • She, H. C., & Chen, Y. Z. (2009). The impact of multimedia effect on science learning: Evidence from eye movements. Computers & Education, 53, 1297–1307.

    Article  Google Scholar 

  • Slykhuis, D. A., Wiebe, E. N., & Annetta, L. A. (2005). Eye-tracking students’ attention to PowerPoint photographs in a science education setting. Journal of Science Education and Technology, 14(5), 509–520.

    Article  Google Scholar 

  • Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145.

    Article  Google Scholar 

  • Tai, R. H., Loehr, J. F., & Brigham, F. J. (2006). An exploration of the use of eye-gaze tracking to study problem-solving on standardized science assessments. International Journal of Research & Method in Education, 29(2), 185–208.

    Article  Google Scholar 

  • Taylor, K. L., & Dionne, J. P. (2000). Accessing problem-solving strategy knowledge: The complementary use of concurrent verbal protocols and retrospective debriefing. Journal of Educational Psychology, 92(3), 413.

    Article  Google Scholar 

  • Terzer, E., Patzke, C., & Upmeier zu Belzen, A. (2012). Validierung von multiple-choice items zur Modellkompetenz durch lautes Denken [Validation of multiple choice items on modeling competence through think aloud]. In U. Harms & F. X. Bogner (Eds.), Lehr- und Lernforschung in der Biologiedidaktik. Innsbruck, Austria: Studienverlag.

    Google Scholar 

  • 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(4), 357–368.

    Article  Google Scholar 

  • Trier, U., Krüger, D., & Upmeier zu Belzen, A. (2014). Students’ versus scientists’ conceptions of models and modelling. In D. Krüger & M. Ekborg (Eds.), Research in biological education (pp. 103–115). Brunswick, Germany: Westermann.

    Google Scholar 

  • Tsai, M. J., Hou, H. T., Lai, M. L., Liu, W. Y., & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58, 375–385.

    Article  Google Scholar 

  • Ubben, I., Nitz, S., Daniel, K. L., & Upmeier zu Belzen, A. (2018). Assessing representational competence with eye tracking technology. In K. L. Daniel (Ed.), Towards a framework for representational competence in science education. New York: Springer.

    Google Scholar 

  • Ubben, I., Nitz, S., & Upmeier zu Belzen, A. (2017, August). Visual perception of phylogenetic trees as models of and for evolution. Poster at ESERA 2017 Conference, Ireland.

    Google Scholar 

  • Ubben, I., Nitz, S., & Upmeier zu Belzen, A. (2019, August). Influence of experience on tree Reading performance. Italy: Poster at ESERA 2019 Conference.

    Google Scholar 

  • Upmeier zu Belzen, A., & Krüger, D. (2010). Modellkompetenz im Biologieunterricht [Modeling competence in biology education]. ZfDN, 16, 41–57.

    Google Scholar 

  • van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25(3), 785–791.

    Article  Google Scholar 

  • van Gog, T., Paas, F., van Merriënboer, J. J. G., & Witte, P. (2005). Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting. Journal of Experimental Psychology. Applied, 11(4), 237–244.

    Article  Google Scholar 

  • van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994). The think aloud method: A practical guide to modelling cognitive processes. London: Academic.

    Google Scholar 

  • Wilson, J., & Clarke, D. (2004). Towards the modelling of mathematical metacognition. Mathematics Education Research Journal, 16(2), 25–48.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Humboldt-Universität zu Berlin, Berlin, Germany

    Inga Ubben

  2. Middle Tennessee State University, Murfreesboro, TN, USA

    Sara L. Salisbury

  3. Texas State University, San Marcos, TX, USA

    Kristy L. Daniel

Authors
  1. Inga Ubben
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara L. Salisbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kristy L. Daniel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Inga Ubben .

Editor information

Editors and Affiliations

  1. Department of Biology, Biology Education, Humboldt-Universität zu Berlin, Berlin, Germany

    Annette Upmeier zu Belzen

  2. Department of Biology, Biology Education, Freie Universität Berlin, Berlin, Germany

    Dirk Krüger

  3. Department of Education, Melbourne University, Melbourne, Australia

    Jan van Driel

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

Ubben, I., Salisbury, S.L., Daniel, K.L. (2019). Combining Visual and Verbal Data to Diagnose and Assess Modeling Competence. 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_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30255-9_6

  • 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

Search

Navigation