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Challenging Students’ Intuitions—the Influence of a Tangible Model of Virus Assembly on Students’ Conceptual Reasoning About the Process of Self-Assembly

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Abstract

A well-ordered biological complex can be formed by the random motion of its components, i.e. self-assemble. This is a concept that incorporates issues that may contradict students’ everyday experiences and intuitions. In previous studies, we have shown that a tangible model of virus self-assembly, used in a group exercise, helps students to grasp the process of self-assembly and in particular the facet “random molecular collision”. The present study investigates how and why the model and the group exercise facilitate students’ learning of this particular facet. The data analysed consist of audio recordings of six group exercises (n = 35 university students) and individual semi-structured interviews (n = 5 university students). The analysis is based on constructivist perspectives of learning, a combination of conceptual change theory and learning with external representations. Qualitative analysis indicates that perceived counterintuitive aspects of the process created a cognitive conflict within learners. The tangible model used in the group exercises facilitated a conceptual change in their understanding of the process. In particular, the tangible model appeared to provide cues and possible explanations and functioned as an “eye-opener” and a “thinking tool”. Lastly, the results show signs of emotions also being important elements for successful accommodation.

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References

  • Alvermann, D. E., Hynd, C. E., & Qian, G. (1995). Effects of interactive discussions and text type on learning counterintuitive science concepts. The Journal of Educational Research, 88(3), 146–155.

    Article  Google Scholar 

  • Ausubel, D. (1968). Educational psychology—a cognitive view. New York: Holt, Rinehart and Winston.

    Google Scholar 

  • Banerjee, A. C. (1995). Teaching chemical equilibrium and thermodynamics in undergraduate general chemistry classes. Journal of Chemical Education, 72(10), 879–881.

    Article  Google Scholar 

  • Berlyne, D. E. (1965). Curiosity and education. In J. D. Krumboltz (Ed.), Learning and the educational process. Chicago: Rand McNally.

    Google Scholar 

  • Boyer, P. (1994). The naturalness of religious ideas: a cognitive theory of religion. Berkeley, CA: University of California Press.

    Google Scholar 

  • Campbell, D. J., Freidinger, E. R., Hastings, J. M., & Querns, M. K. (2002). Spontaneous assembly of soda straws. Journal of Chemical Education, 79(2), 201.

    Article  Google Scholar 

  • Campbell, D. J., Freidinger, E. R., & Querns, M. K. (2001). Spontaneous assembly of magnetic LEGO bricks. The Chemical Educator, 6(6), 321–323.

    Article  Google Scholar 

  • Cousin, G. (2006). An introduction to threshold concepts. Planet, 17(December), 4–5.

    Article  Google Scholar 

  • D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145–157.

    Article  Google Scholar 

  • Dochy, F., de Rijdt, C., & Dyck, W. (2002). Cognitive prerequisites and learning. How far have we progressed since bloom? Implications for educational practice and teaching. Active Learning in Higher Education, 3(3), 265–284.

    Article  Google Scholar 

  • Dochy, F. J. R. C., Moerkerke, G., & Martens, R. (1996). Integrating assessment, learning, and instruction: assessment of domain-specific and domain-transcending prior knowledge and process. Studies in Educational Evaluation, 22(4), 309–339.

    Article  Google Scholar 

  • Dochy, F., Segers, M., & Buehl, M. M. (1999). The relation between assessment practices and outcomes of studies: the case of research on prior knowledge. Review of Educational Research, 69(2), 145–186.

    Article  Google Scholar 

  • Dori, Y. J., & Barak, M. (2001). Virtual and physical molecular modeling: fostering model perception and spatial understanding. Educational Technology & Society, 4(1), 61–74.

    Google Scholar 

  • Driver, R., Guesne, E., & Tiberghien, A. (1985). Children’s ideas and the learning of science. In R. Driver, E. Guesne, & A. Tiberghien (Eds.), Children’s ideas in science (pp. 1–9).

  • Driver, R., Squires, A., Rushworth, P., & Wood-Robinson, V. (1994). Making sense of secondary science: research into children’s ideas. London: Routledge.

    Google Scholar 

  • Duit, R. H. & Treagust, D. F. (2012). Conceptual change: still a powerful framework for improving the practice of science instruction. In K. C. D. Tan & M. Kim (Eds.), Issues and challenges in science education research (pp. 43–54): Springer Netherlands.

  • Elby, A. (2000). What students’ learning of representations tells us about constructivism. Journal of Mathematical Behavior, 19, 481–502.

    Article  Google Scholar 

  • Feltovich, P. J., Spiro, R. J., & Coulson, R. L. (1989). The nature of conceptual understanding in biomedicine: the deep structure of complex ideas and the development of misconceptions. In D. Evans & V. Patel (Eds.), Cognitive science in medicine: biomedical modeling. Cambridge: MIT Press.

    Google Scholar 

  • Franks, B. (2003). The nature of unnaturalness in religious representations: negations and concept combination. Journal of Cognition and Culture, 3(1), 41–68.

    Article  Google Scholar 

  • Friedler, Y., Amir, R., & Tamir, P. (1987). High school students’ difficulties in understanding osmosis. International Journal of Science Education, 9(5), 541–551.

    Article  Google Scholar 

  • Gabel, D. L., & Samuel, K. V. (1987). Understanding the particular nature of matter. Journal of Chemical Education, 64(8), 695–697.

    Article  Google Scholar 

  • Garvin-Doxas, K., & Klymkowsky, M. W. (2008). Understanding randomness and its impact on student learning: lessons learned from building the biology concept inventory (BCI). CBE-Life Science Education, 7, 227–233.

    Article  Google Scholar 

  • Gibbs, G. (Ed.). (2007). Analyzing qualitative data. Great Britain: SAGE Publications Ltd.

    Google Scholar 

  • Glaser, R. (1983). Education and thinking: the role of knowledge. Technical report no. PDS-6, Pittsburgh: University of Pittsburgh.

  • Glasson, G. E. (1989). The effects of hands-on and teacher demonstration laboratory methods on science achievement in relation to reasoning ability and prior knowledge. Journal of Research in Science Teaching, 26, 121–131.

    Article  Google Scholar 

  • Gordon, M. (1991). Counterintuitive instances encourage mathematical thinking. Mathematics Teacher, 84(7), 511–515.

    Google Scholar 

  • Gordon, D. N., & Pea, R. D. (1995). Prospects for scientific visualization as an educational technology. Journal of Learning Sciences, 4(3), 249–279.

    Article  Google Scholar 

  • Graneheim, U. H., & Lundman, B. (2004). Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–112.

    Article  Google Scholar 

  • Guzzetti, B. J. (2000). Learning counter-intuitive science concepts: what have we learned from over a decade of research? Reading & Writing Quarterly, 16, 89–98.

    Article  Google Scholar 

  • Guzzetti, B. J., Williams, W. O., Skeels, S. A., & Wu, S. M. (1997). Influence of text structure on learning counter-intuitive physics concepts. Journal of Research in Science Teaching, 34, 700–719.

    Article  Google Scholar 

  • Hadjiachilleos, S., Valanides, N., & Angeli, C. (2013). The impact of cognitive and affective aspects of cognitive conflict on learners’ conceptual change about floating and sinking. Research in Science and Technological Education, 31(2), 133–152.

    Article  Google Scholar 

  • Hailikari, T., Nevgi, A., & Lindblom-Ylanne, S. (2007). Exploring alternative ways of assessing prior knowledge, its components and their relation to student achievement: a mathematics based study. Studies in Educational Evaluation, 33(3–4), 320–337.

    Article  Google Scholar 

  • Hammer, D., & Elby, A. (2002). On the form of a personal epistemology. In B. K. Hofer & P. R. Pintrich (Eds.), Personal epistemology: the psychology of beliefs about knowledge and knowing (pp. 169–190). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Harris, M. A., Peck, R. F., Colton, S., Morris, J., Neto, E. C., & Kallio, J. (2009). A combination of hand-held models and computer imaging programs helps students answer oral questions about molecular structure and function: a controlled investigation of student learning. CBE-Life Science Education, 8, 29–43.

    Article  Google Scholar 

  • Harrison, A. G., & Treagust, D. F. (2002). The particulate nature of matter: challenges in understanding the submicroscopic world. In J. K. Gilbert, O. De Jong, R. Justi, D. F. Treagust, & J. H. Van Driel (Eds.), Chemical education: towards research-based practice (pp. 213–234). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Hewson, P. W., & Hewson, M. G. A. B. (1984). The role of cognitive conflict in conceptual change and the design of instruction. Instructional Science, 13(1), 1–13.

    Article  Google Scholar 

  • Howitt, S., Anderson, T. R., Costa, M., Hamilton, S., & Wright, A. (2008). A concept inventory for molecular life sciences: how will it help your teaching practice? Australian Biochemist, 39(3), 14–17.

    Google Scholar 

  • Höst, G. E., Larsson, C., Olson, A., & Tibell, A. E. (2013). Students learning about biomolecular self-assembly using two different external representations. CBE- Life Sciences Education, 12(3), 471–482.

  • Ishii, H. & Ullmer, B. (1997, March). Tangible bits: towards seamless interfaces between people, bits, and atoms. Paper presented at the CHI’97 Conference on Human Factors in Computing Systems, Atlanta, Georgia.

  • Jones, M. G., Falvo, M. R., Broadwell, B., & Dotger, S. (2006). Self-assembly: how nature builds. Designing a model illustrates the basic ideas of self-assembly. The Science Teacher, 73(December), 54–57.

    Google Scholar 

  • Kahneman, D. (2003). Maps of bounded rationality: psychology for behavioural economics. The American Economic Review, 93(5), 1449–1475.

    Article  Google Scholar 

  • Kahneman, D. (2012). Thinking, fast and slow. New York: Farrar Straus Giroux.

    Google Scholar 

  • Kahneman, D., & Frederick, S. (2002). Representativeness revisited: attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: the psychology of intuitive thought (pp. 49–81). New York: Cambridge University Press.

    Chapter  Google Scholar 

  • Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning. The Journal of the Learning Sciences, 9(2), 105–143.

    Article  Google Scholar 

  • Kreuger, R. A., & Casey, M. A. (2000). Focus groups: a practical guide for applied research. London: Sage Publications.

    Book  Google Scholar 

  • Larkin, J. H. (1983). The role of problem representation in physics. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 75–98). Hillsdale: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Larsson, S. (2009). A pluralist view of generalization in qualitative research. International Journal of Research & Method in Education, 32(1).

  • Larsson, C. A., Höst, G. E., Anderson, T. R., & Tibell, L. A. E. (2011). Using a teaching-learning sequence (TLS), based on a physical model, to develop students’ understanding of self-assembly. In A. Yarden & G. S. Carvalho (Eds.), Authenticity in biology education: benefits and challenges. A selection of papers presented at the 8th Conference of European Researchers in Didactics of Biology (ERIDOB), Braga, Portugal (pp. 67–77). Braga, Portugal: CIEC, Universidade do Minho.

  • Lesser, L. (1998). Countering indifference using counterintuitive examples. Teaching Statistics, 20(1), 10–12.

    Article  Google Scholar 

  • Lindsey, J. S. (1991). Self-assembly in synthetic routes to molecular devices. Biological principles and chemical perspectives: a review. New Journal of Chemistry, 15, 153–180.

    Google Scholar 

  • Marshall, P. (2007). Do tangible interfaces enhance learning? Paper presented at the 1st International Conference on Tangible and Embedded Interaction, Baton Rouge, Louisiana, USA, 15–17 February

  • Marshall, P., Rogers, Y., & Hornecker, E. (2007). Are tangible interfaces really any better than other kinds of interfaces? Paper presented at the CHI’07 Workshop on Tangible User Interfaces in Context & Theory, San Jose, California, USA, 28 April

  • McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 299–324). Hillsdale: Erlbaum.

    Google Scholar 

  • McDermott, L. C. (1993). How we teach and how students learn—a mismatch? American Journal of Physics, 61(4).

  • Méheut, M., & Psillos, D. (2004). Teaching-learning sequences: aims and tools for science education research. International Journal of Science Education, 26(5), 515–536.

    Article  Google Scholar 

  • Minogue, J., & Jones, M. G. (2006). Haptics in education: exploring an untapped sensory modality. Review of Educational Research, 76(3), 317–348.

    Article  Google Scholar 

  • Montessori, M. (1912). The montessori method. New York: Frederick A. Stokes Company.

    Google Scholar 

  • Nakhleh, M. B. (1992). Why some students don’t learn chemistry. Journal of Chemical Education, 69(3), 191–196.

    Article  Google Scholar 

  • Nakhleh, M. B., Samarapungavan, A., & Saglam, Y. (2005). Middle school students’ beliefs about matter. Journal of Research in Science Teaching, 42(5), 581–612.

    Article  Google Scholar 

  • Novick, S., & Nussbaum, J. (1978). Junior high school pupils’ understanding of the particulate nature of matter: an interview study. Science Education, 62(3), 273–281.

    Article  Google Scholar 

  • Nussbaum, J., & Novick, S. (1982). Alternative frameworks, cognitive conflict and accommodation: toward a principled teaching strategy. Instructional Science, 11, 183–200.

    Article  Google Scholar 

  • Odom, A. L. (1995). Secondary and college biology students’ misconceptions about diffusion and osmosis. The American Biology Teacher, 57, 409–415.

    Article  Google Scholar 

  • Olson, A. J., Hu, Y. H. E., & Keinan, E. (2007). Chemical mimicry of viral capsid self-assembly. Proceedings of the National Academy of Sciences, 104, 20731–20736.

    Article  Google Scholar 

  • O’Malley, C., & Stanton Fraser, D. (2004). Literature review in learning with tangible technologies. Nesta Futurelab Series (pp. 1–48).

  • Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: designing an elbow. Journal of Research in Science Teaching, 34, 1–20.

    Article  Google Scholar 

  • Perkins, D. (1999). The many faces of constructivism. Educational Leadership, 57(3), 6–11.

    Google Scholar 

  • Pintrich, P. R., & Schrauben, B. (1992). Students’ motivational beliefs and their cognitive engagement in classroom academic tasks. In D. H. Schunk & J. L. Meece (Eds.), Student perceptions in the classroom (pp. 149–183). Hillsdale: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Price, S., Rogers, Y., Scaife, M., Stanton, D., & Neale, H. (2003). Using ‘tangibles’ to promote novel forms of playful learning. Interacting with Computers, 15(2), 169–185.

    Article  Google Scholar 

  • Redish, E. (1994). The implications of cognitive studies for teaching physics. American Journal of Physics, 62(9), 796–803.

    Article  Google Scholar 

  • Resnick, M. (1994). Turtles, termites and traffic jams: explorations in massively parallel microworlds. Cambridge: MIT Press.

    Google Scholar 

  • Resnick, M. (1996). Beyond the centralized mindset. Journal of the Learning Sciences, 5(1), 1–22.

    Article  Google Scholar 

  • Resnick, M., & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: new thinking for new sciences. Paper presented at the Annual Conference of the American Educational Research Association, Atlanta, GA.

  • Rittle-Johnson, B., Siegler, R. S., & Wagner Alibali, M. (2001). Developing conceptual understanding and procedural skill in mathematics: an iterative process. Journal of Educational Psychology, 93(2), 346–362.

    Article  Google Scholar 

  • Roberts, J. R., Hagedorn, E., Dillenburg, P., Patrick, M., & Herman, T. (2005). Physical models enhance molecular three-dimensional literacy in an introductory biochemistry course. Biochemistry and Molecular Biology Education, 33(2), 105–110.

    Article  Google Scholar 

  • Rotbain, Y., Marbach-Ad, G., & Stavy, R. (2006). Effect of bead and illustrations models on high school students’ achievement in molecular genetics. Journal of Research in Science Education, 43(5), 500–529.

    Google Scholar 

  • Ryan, G. W., & Bernard, R. H. (2003). Techniques to identify themes. Field Methods, 15(1), 85–109.

    Article  Google Scholar 

  • Sathian, K. (1998). Perceptual learning. Current Science, 75, 451–456.

    Google Scholar 

  • Schönborn, K. J., Haglund, J., & Xie, C. (2012). “But metal really is just colder!” Using thermoimaging in augmented multisensory learning to induce cognitive conflict about heat transfer. Paper presented at the World Conference on Physics Education, Istanbul, Turkey, July 1–6.

  • Sears, D. (2008). Moving toward a biochemistry concept inventory. American Society for Biochemistry and Molecular Biology (September), 19–21. http://www.asbmb.org/uploadedFiles/ASBMBToday/Content/Archive/ASBMBToday-September-2008.pdf

  • Smith, J. P., III, diSessa, A., & Roschelle, J. (1994). Misconceptions reconceived: a constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115–163.

    Article  Google Scholar 

  • Stavy, R. (1991). Children’s ideas about matter. School Science and Mathematics, 91, 240–244.

    Article  Google Scholar 

  • Tirosh, D., Stavy, R., & Cohen, S. (1998). Cognitive conflict and intuitive rules. International Journal of Science Education, 20(10), 1257–1269.

    Article  Google Scholar 

  • Upal, M. A., Gonce, L. O., Tweney, R. D., & Slone, D. J. (2007). Contextualizing counterintuitiveness: how context affects comprehension and memoriability of counterintuitive concepts. Cognitive Science, 31, 415–439.

    Article  Google Scholar 

  • Vesilind, E. M., & Jones, G. M. (1996). Hands-on: science education reform. Journal of Teacher Education, 47(5), 375–385.

    Article  Google Scholar 

  • Webb, P., & Treagust, D. F. (2006). Using exploratory talk to enhance problem-solving and reasoning skills in grade-7 science classrooms. Research in Science Education, 36, 381–401.

    Article  Google Scholar 

  • Westbrook, S. L., & Marek, E. A. (1991). A cross-age study of student understanding of the concept of diffusion. Journal of Research in Science Teaching, 28(8), 649–660.

    Article  Google Scholar 

  • Whitesides, G. M., Mathias, J. P., & Seto, C. T. (1991). Molecular self-assembly and nanochemistry: a chemical strategy for the synthesis of nanostructures. Science, 254(5036), 1312–1319.

    Article  Google Scholar 

  • 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 

  • Zuckerman, O., Arida, S., & Resnick, M. (2005). Extending tangible interfaces for education: digital montessori-inspired manipulatives. Paper presented at the Conference on Human Factors in Computing Systems (CHI), Portland, Oregon, April 2–7.

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Acknowledgments

The authors would like to thank the participating students for their engagement and Professor Trevor Anderson (Purdue University, West Lafayette, IN, USA), Associate Professor Magdalena Svensson (Linköping University, Sweden) and Dr. Gunnar Höst (Linköping University, Sweden) for assistance in the data collection procedure. We are also grateful to our colleagues at Linköping University for valuable discussions and Professor Arthur Olson (Scripps Research Institute, San Diego, USA) for providing the tangible models of virus self-assembly.

Funding

The Swedish Research Council (VR 2008–5077, principal investigator Lena Tibell) supported this research.

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Correspondence to Caroline Larsson.

Appendices

Appendix 1

Discussion Guide

Consider the model of a virus capsid in the container. It consists of twelve subunits. In reality, each of the subunits is composed of five identical proteins. Thus, the complete capsid consists of 60 protein molecules.

Task 1

Remove the subunits from the container and try to assemble the subunits into a complete virus capsid by hand.

  • How do you think such virus capsids assemble in reality?

Task 2

Break the capsid and place the subunits in the container, and then close the container. Now try to assemble the subunits into a complete virus capsid.

  • Does each subunit always end up in the same place in the capsid?

  • Do the subunits attach to the growing capsid in the same order each time it assembles?

  • What makes it possible for a subunit to bind to another subunit?

  • Do you think that the assembly process is random or guided? Why?

Task 3

Try to simulate an increased temperature in the container. Then try again to assemble the subunits into a complete capsid under the high-temperature condition.

Try to simulate a decreased temperature in the container. Then try again to assemble the subunits into a complete capsid under the low-temperature condition.

  • How is the process of self-assembly influenced by temperature?

  • Why does the process progress differently at the different temperatures?

Task 4

The formation of each bond between the subunits is reversible. Next time you assemble the subunits into a capsid, take extra notice of any cases where a subunit detaches from another subunit or a complex of subunits.

  • Why do subunits detach from each other?

Task 5

Open the container and pick up the subunits. Examine the stability of a complex consisting of two subunits. Then add one more subunit and examine the stability of the three-piece complex. Finally, assemble the complete capsid and examine the stability.

  • Can you feel any differences in the stability between the different complexes?

  • How is the capsid stability influenced by temperature?

  • What factors determine the thermodynamic stability?

Task 6

Two subunits might attach to each other in the wrong way. Put the subunits back into the container. The next time you assemble the capsid, make pauses to observe the different complexes between subunits. Take extra notice of any wrongly formed complexes.

  • What happens to the wrongly formed complexes during assembly?

  • How does the error correction mechanism work?

Task 7

It is important to also consider the limitations of a model. Although a model might clarify and explain certain aspects of a phenomenon, no model is a perfect reflection of reality. In relation to the physical model used here, consider the following questions:

  • What other environmental factors, besides temperature, can influence the process of self-assembly in virus-production in a cell?

  • What limitations or simplifications can you see in the physical model?

  • What is the significance of the container dimensions?

Appendix 2

Interview Guide

Preparation

  1. 1.

    Welcome

  2. 2.

    Permission to make audio recordings – Informed consent form

Recording Starts

  1. 1.

    Date

  2. 2.

    Name

  3. 3.

    Use of visualizations

    1. a.

      Do you usually look at the text, the picture, or both text and picture first when learn about something?

    2. b.

      Do you usually use pictures, models etc.? If so, how?

    3. c.

      Do you find it hard or easy to understand pictures, models, animations etc?

  4. 4.

    Self-assembly tutorial

    1. a.

      How did you experience the group-exercise? Did you learn anything new during the discussion?

  5. 5.

    The concept of self-assembly

    1. a.

      Did you know anything about self-assembly before this tutorial?

    2. b.

      Did you learn anything new? What? When?

    3. c.

      Is it something in particular that you find hard/difficult to understand regarding self-assembly?

    4. d.

      Use virus capsid assembly as an example and ask the student to explain the following facets:

      1. i.

        Random molecular collisions

      2. ii.

        Reversibility

      3. iii.

        Influence of temperature

      4. iv.

        Differential stability

      5. v.

        Error correction

  6. 6.

    Please, comment your answer on statement 8.

  7. 7.

    Individual questions

Finishing

  1. 1.

    Turn of recording equipment

  2. 2.

    Thank you!

  3. 3.

    Questions?

Appendix 3

Transcript from Group Discussion

A transcript retrieved from a group discussion that shows a section where students discuss the random molecular collision facet of the self-assembly process.

Group 3

The group has disassembled the tangible virus model and placed it in the container and then started to shake it.

S3: Cool!

S4: (Laughs)

S1: (Laughs)

Discussion facilitator: … but you can start thinking about how, then I said they are gathered together and assemble but how does that happen in the cell? Do you have any idea?

S1: It feels like they have to make use of someone, some sort of enzyme so it goes relatively fast… (M2: mm)

S2: They are standing there, prepared to (M1: mm, yes) be placed correctly…

S1: We read a little about that, in microbiology but there it was more about the strands of DNA and RNA, and such (not clear)

S2: (not clear)

S1: Some sort of enzyme.

Discussion facilitator: Some sort of enzyme (KX: mm) do you agree on that everyone?

S5: Yes

S1: ((Laughter))

S6: ((Laughter)) yes

S2: It feels like it is needed, then it should be like that, because otherwise things would had been much slower than, than what they would have done without the enzyme (S?: mm) and then had some things (inaudible), anyhow in the body, not functioning as they should

Students start to shake the tangible virus model.

S1: Shake it, shake it, shake it (KX: not clear).

S2: Perhaps it is too harsh… but it should not be (not clear) yes perhaps (not clear).

S3: You should try.

S1: (Laughs) Try! Now it is really hot, yes.

S2: No, you should try, it works, it will be all right.

S1: (Laughs) But you’ve got the right approach, thus (S2: mm) but it was

S5: (Laughs) (not clear)

S1: (not clear) Nice! Now I am cheating (S2: Laughs) I must try some random here, otherwise it will be cheating (S2: Laughs) yes.

S5: We may not shake too hard on them (S6: no) because then they will brake again.

S6: oops, oh boy

S2: But now then, now you might get it together without it being stuck in there?

S6: Yes.

S3: Oh, one piece left (K6: Laughs)

S1: It feels like as it is like a big, big, because viruses are so small it feels, after all, that it has to be close for it to be able to go together like that and of itself (S2: yes) so it is not scattered

S2: That’s true.

Discussion facilitator: Do you think that uh, that they follow a specific route or how do they assemble?

S3: (shaking the model) No! (Laughter) continue.

S2: I think it’s like this, that random collisions seem, if you look at it, how we shake and keep doing it, it’s random that this piece fits at that specific collision that seems to get stuck (S3: mm) (S4: mm) (S1: mm) (Discussion facilitator: mm) (S6: not clear)

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Larsson, C., Tibell, L.A.E. Challenging Students’ Intuitions—the Influence of a Tangible Model of Virus Assembly on Students’ Conceptual Reasoning About the Process of Self-Assembly. Res Sci Educ 45, 663–690 (2015). https://doi.org/10.1007/s11165-014-9446-6

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