Skip to main content
Log in

Quantification in Empirical Activity

Tracing Children’s Interests and Ideas

  • Article
  • Published:
Science & Education Aims and scope Submit manuscript

Abstract

Changing where, when, and how objects are studied is central to lab-based science (Knorr Cetina, 1999). Science involves changing the scale of objects—particularly scales of size, time, and intensity—from what is experienced in the world. Similar to investigations conducted in science laboratories, classroom investigations involve re-representing and re-scaling entities, manipulating them, and observing effects in new locations and timescales. However, this aspect of investigation is under-studied and under-utilized as a resource for learning. We argue that, from elementary school, children can experience quantification, or identifying, developing, and working with variables, as consequential and can take up differences in representation and scale in empirical investigations as opportunities for sense-making and conceptual progress. We describe two instantiations of an investigation into heating and cooling, showing that 7- and 8-year-old students oriented to gaps and ambiguities related to temperature and that the redesign supported children and teachers to take up temperature for productive sense-making and conceptual progress. We examine opportunities for quantification across the heating and cooling investigation and a second investigation into landforms. This work has implications for supporting quantification in science activity in the early grades and using empirical investigations as opportunities for sense-making.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of Data and Material

Identifiable video data is confidential per Human Subjects protocols.

Code Availability

Not applicable.

Notes

  1. All teachers’ and children’s names are pseudonyms.

  2. Following Jin et al. (2019), we use these terms interchangeably.

References

  • Abd-El-Khalick, F. (2008). Modeling science classrooms after scientific laboratories: Recommendations for research and implementation. In R. Duschl & R. Grandy (Eds.), Teaching scientific inquiry (pp. 80–85). Sense Publishers.

    Chapter  Google Scholar 

  • Achieve, Inc. (2013). Next generation science standards. The Next Generation Science Standards.

  • Bang, M., Warren, B., Rosebery, A. S., & Medin, D. (2012). Desettling expectations in science education. Human Development, 55(5–6), 302–318. https://doi.org/10.1159/000345322

    Article  Google Scholar 

  • Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2016). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(7), 1082–1112. https://doi.org/10.1002/tea.21257

    Article  Google Scholar 

  • Chang, H. (2004). Inventing temperature: Measurement and scientific progress. Oxford University Press.

  • Chen, Y.-C. (2020). Dialogic pathways to manage uncertainty for productive engagement in scientific argumentation: A longitudinal case study grounded in an ethnographic perspective. Science & Education, 29(2), 331–375. https://doi.org/10.1007/s11191-020-00111-z

    Article  Google Scholar 

  • Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86(2), 175–218. https://doi.org/10.1002/sce.10001

    Article  Google Scholar 

  • Clark, D. B. (2006). Longitudinal conceptual change in students’ understanding of thermal equilibrium: An examination of the process of conceptual restructuring. Cognition and Instruction, 24(4), 467–563. https://doi.org/10.1207/s1532690xci2404_3

    Article  Google Scholar 

  • Cobb, P., Confrey, J., diSessa, A. A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009

    Article  Google Scholar 

  • Cobb, P., Stephan, M., McClain, K., & Gravemeijer, K. (2001). Participating in classroom mathematical practices. The Journal of the Learning Sciences, 10(1–2), 113–163. https://doi.org/10.1207/S15327809JLS10-1-2_6

    Article  Google Scholar 

  • Duschl R, Avraamidou L, Azevedo NH (2021). Data-texts in the sciences: The evidence-explanation continuum. Science & Education 1159–1181https://doi.org/10.1007/s11191-021-00225-y

  • Engle, R. A. (2011). The productive disciplinary engagement framework: Origins, key concepts, and developments. In D. Dai (Ed.), Design research on learning and thinking in educational settings: Enhancing growth and functioning (pp. 170–209). Routledge.

    Google Scholar 

  • Engle, R. A., & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20(4), 399–483. https://doi.org/10.1207/S1532690XCI2004_1

    Article  Google Scholar 

  • Erickson, G., & Tiberghien, A. (1985). Heat and temperature. In R. Driver, E. Guesne, & A. Tiberghien (Eds.), Children’s ideas in science (pp. 52–84). Open University Press.

    Google Scholar 

  • Ford, M. J. (2005). The game, the pieces, and the players: Generative resources from two instructional portrayals of experimentation. Journal of the Learning Sciences, 14(4), 449–487. https://doi.org/10.1207/s15327809jls1404_1

    Article  Google Scholar 

  • Furtak, E. M., Shavelson, R., Shemwell, J., Figueroa, M., Carver, S., & Shrager, J. (2012). To teach or not to teach through inquiry: Is that the question. In S. Carver & J. Shrager (Eds.), The journey from child to scientist: Integrating cognitive development and the education sciences (pp. 227–244). American Psychological Association.

    Chapter  Google Scholar 

  • Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–405. https://doi.org/10.1037/0022-0663.95.2.393

    Article  Google Scholar 

  • Giere, R. N. (1990). Explaining science: A cognitive approach. University of Chicago Press.

  • Gooding, D. (1990). Experiment and the making of meaning. Kluwer Academic Publishers.

    Book  Google Scholar 

  • Gouvea, J., & Passmore, C. (2017). Models of’ versus ‘models for. Science & Education, 26(1), 49–63. https://doi.org/10.1007/s11191-017-9884-4

    Article  Google Scholar 

  • Grotzer, T. A., Derbiszewska, K., & Solis, S. L. (2017). Leveraging fourth and sixth graders’ experiences to reveal understanding of the forms and features of distributed causality. Cognition and Instruction, 35(1), 55–87. https://doi.org/10.1080/07370008.2016.1251808

    Article  Google Scholar 

  • Gutiérrez, K. D., & Rogoff, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher, 32(5), 19–25. https://doi.org/10.3102/0013189X032005019

    Article  Google Scholar 

  • Hammer, D., & Berland, L. K. (2014). Confusing claims for data: A critique of common practices for presenting qualitative research on learning. Journal of the Learning Sciences, 23(1), 37–46. https://doi.org/10.1080/10508406.2013.802652

    Article  Google Scholar 

  • Hamza, K. M., & Wickman, P. (2009). Beyond explanations: What else do students need to understand science? Science Education, 93(6), 1026–1049. https://doi.org/10.1002/sce.20343

    Article  Google Scholar 

  • Hesse, M. (1966). Models and analogies in science. University of Notre Dame Press.

    Google Scholar 

  • Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368

    Article  Google Scholar 

  • Jin, H., Delgado, C., Bauer, M. I., Wylie, E. C., Cisterna, D., & Llort, K. F. (2019). A hypothetical learning progression for quantifying phenomena in science. Science & Education, 28(9), 1181–1208. https://doi.org/10.1007/s11191-019-00076-8

    Article  Google Scholar 

  • Karpudewan, M., Roth, W.-M., & Abdullah, M. N. S. B. (2015). Enhancing primary school students’ knowledge about global warming and environmental attitude using climate change activities. International Journal of Science Education, 37(1), 31–54. https://doi.org/10.1080/09500693.2014.958600

    Article  Google Scholar 

  • Keifert, D., & Stevens, R. (2019). Inquiry as a members’ phenomenon: Young children as competent inquirers. Journal of the Learning Sciences, 28(2), 240–278. https://doi.org/10.1080/10508406.2018.1528448

    Article  Google Scholar 

  • Kline, M. (1980). Mathematics: The loss of certainty. Oxford University Press.

  • Knorr Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Harvard University Press.

  • Kuo, E., Hull, M. M., Gupta, A., & Elby, A. (2013). How students blend conceptual and formal mathematical reasoning in solving physics problems. Science Education, 97(1), 32–57. https://doi.org/10.1002/sce.21043

    Article  Google Scholar 

  • Lehrer, R., Giles, N., & Schauble, L. (2002). Data modeling. In R. Lehrer & L. Schauble (Eds.), Investigating real data in the classroom: Expanding children’s understanding of math and science. (pp. 1-26). Teachers College Press.

  • Lehrer, R., & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96(4), 701–724. https://doi.org/10.1002/sce.20475

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. In L. Liben & U. Müller (Eds.), Handbook of child psychology and developmental science (Vol. 2, p. 671–714).

  • Leinhardt, G., Zaslavsky, O., & Stein, M. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching. Review of Educational Research, 60(1), 1–64.

  • Lewis, E. L., & Linn, M. C. (1994). Heat energy and temperature concepts of adolescents, adults, and experts: Implications for curricular improvements. Journal of Research in Science Teaching, 31(6), 657–677. https://doi.org/10.1002/tea.3660310607

    Article  Google Scholar 

  • Lynch, M., & Macbeth, D. (1998). Demonstrating physics lessons. In J. G. Greeno & S. V. Goldman (Eds.), Thinking practices in mathematics and science learning (pp. 269–297). Erlbaum Press.

    Google Scholar 

  • Manz, E. (2015a). Representing student argumentation as functionally emergent from scientific activity. Review of Educational Research, 85(4), 553–590. https://doi.org/10.3102/0034654314558490

    Article  Google Scholar 

  • Manz, E. (2015b). Resistance and the development of scientific practice: Designing the mangle into science instruction. Cognition and Instruction., 33(2), 89–124. https://doi.org/10.1080/07370008.2014.1000490

    Article  Google Scholar 

  • Manz, E. (2018). Designing for and analyzing productive uncertainty in science investigations. In J. Kay & R. Luckin (Eds.), Rethinking learning in the digital age: Making the learning sciences count. 13th International Conference of the Learning Sciences (ICLS), London, UK.

  • Manz, E., Lehrer, R., & Schauble, L. (2020). Rethinking the classroom science investigation. Journal of Research in Science Teaching, 57(7), 1148–1174. https://doi.org/10.1002/tea.21625

    Article  Google Scholar 

  • Metz, K. E. (2004). Children’s understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22(2), 219–290. https://doi.org/10.1207/s1532690xci2202_3

    Article  Google Scholar 

  • Metz, K. E. (2011). Disentangling robust developmental constraints from the instructionally mutable: Young children’s epistemic reasoning about a study of their own design. Journal of the Learning Sciences, 20(1), 50–110. https://doi.org/10.1080/10508406.2011.529325

    Article  Google Scholar 

  • National Academies of Sciences, Engineering, and Medicine 2021. Science and engineering in preschool through elementary grades: The brilliance of children and the strengths of educators. Washington, DC: The National Academies Press. https://doi.org/10.17226/26215.

  • National Research Council (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press.

  • Nersessian, N. J. (2008). Creating scientific concepts. The MIT Press.

    Book  Google Scholar 

  • Nersessian, N. J. (2012). Engineering concepts: The interplay between concept formation and modeling practices in bioengineering sciences. Mind, Culture, and Activity, 19(3), 222–239. https://doi.org/10.1080/10749039.2012.688232

    Article  Google Scholar 

  • Odden, T. O. B., & Russ, R. S. (2018). Defining sensemaking: Bringing clarity to a fragmented theoretical construct. Science Education, 103(1), 187–205. https://doi.org/10.1002/sce.21452

    Article  Google Scholar 

  • Osborne, J., Rafanelli, S., & Kind, P. (2018). Toward a more coherent model for science education than the crosscutting concepts of the next generation science standards: The affordances of styles of reasoning. Journal of Research in Science Teaching, 55(7), 962–981. https://doi.org/10.1002/tea.21460

    Article  Google Scholar 

  • Penuel, W. R., Roschelle, J., & Shechtman, N. (2007). Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning, 2(01), 51–74. https://doi.org/10.1142/S1793206807000300

    Article  Google Scholar 

  • Phillips, A. M., Watkins, J., & Hammer, D. (2017). Problematizing as a scientific endeavor. Physical Review Physics Education Research, 13(2), 020107. https://doi.org/10.1103/PhysRevPhysEducRes.13.020107

    Article  Google Scholar 

  • Planinic, M., Milin-Sipus, Z., Katic, H., Susac, A., & Ivanjek, L. (2012). Comparison of student understanding of line graph slope in physics and mathematics. International Journal of Science and Mathematics Education, 10(6), 1393–1414. https://doi.org/10.1007/s10763-012-9344-1

    Article  Google Scholar 

  • Rosebery, A., & Hudicourt-Barnes, J. (2006). Using diversity as a strength in the science classroom: The benefits of science talk. In R. Douglas, M. Klentschy, & K. Worth (Eds.), Linking science & literacy in the K-8 classroom (pp. 305–320). National Science Teachers Association.

    Google Scholar 

  • Rosebery, A. S., Ogonowski, M., DiSchino, M., & Warren, B. (2010). “The coat traps all your body heat”: Heterogeneity as fundamental to learning. THe Journal of the Learning Sciences, 19(3), 322–357. https://doi.org/10.1080/10508406.2010.491752

    Article  Google Scholar 

  • Rouse, J. (2015). Articulating the world: Conceptual understanding and the scientific image. University of Chicago Press.

  • Russ, R. S. (2014). Epistemology of science vs. epistemology for science. Science Education, 98(3), 388–396. https://doi.org/10.1002/sce.21106

  • Sarama, J., Brenneman, K., Clements, D. H., Duke, N. K., & Hemmeter, M. L. (2017).Interdisciplinary teaching across multiple domains: The C4L (Connect4Learning) curriculum. In L. B. Bailey (Ed.), Implementing a standards-based curriculum in the early childhood classroom (pp. 1-53). Routledge.

  • Sarama, J., Clements, D. H., Baroody, A. J., Kutaka, T. S., Chernyavskiy, P., Shi, J., & Cong, M. (2021). Testing a theoretical assumption of a learning-trajectories approach in teaching length measurement to kindergartners. AERA Open, 7. Online View. https://doi.org/10.1177/23328584211026657

  • Schauble, L., Glaser, R., Duschl, R. A., Schulze, S., & John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. Journal of the Learning Sciences, 4(2), 131–166. https://doi.org/10.1207/s15327809jls0402_1

    Article  Google Scholar 

  • Schwarz, C. V., Passmore, C., & Reiser, B. J. (2017). Helping students make sense of the world using next generation science and engineering practices. NSTA Press.

  • Severance, S., Penuel, W. R., Sumner, T., & Leary, H. (2016). Organizing for teacher agency in curricular co-design. Journal of the Learning Sciences, 25(4), 531–564. https://doi.org/10.1080/10508406.2016.1207541

    Article  Google Scholar 

  • Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic-molecular theory. Measurement: Interdisciplinary Research & Perspective, 4(1–2), 1–98. https://doi.org/10.1080/15366367.2006.9678570

  • Tuminaro, J., & Redish, E. F. (2007). Elements of a cognitive model of physics problem solving: Epistemic games. Physical Review Physics Education Research, 3(2), 020101. https://doi.org/10.1103/PhysRevSTPER.3.020101

    Article  Google Scholar 

  • Tytler, R., Mulligan, J., Prain, V., White, P., Xu, L., Kirk, M., Nielsen, C., & Speldewinde, C. (2021). An interdisciplinary approach to primary school mathematics and science learning. International Journal of Science Education, 43(12), 1926–1949. https://doi.org/10.1080/09500693.2021.1946727

    Article  Google Scholar 

  • Warren, B., Ballenger, C., Ogonowski, M., Rosebery, A. S., & Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching, 38(5), 529–552. https://doi.org/10.1002/tea.1017

    Article  Google Scholar 

  • Wiser, M., Smith, C. L., & Doubler, S. (2012). Learning progressions as tools for curriculum development: Lessons from the Inquiry Project. In A. C. Alonzo & A. W. Gotwals (Eds.), Learning progressions in science: Current challenges and future directions (pp. 357–403). Sense Publishers. https://doi.org/10.1007/978-94-6091-824-7_16

Download references

Acknowledgements

The heating and cooling investigation described here was designed and refined in partnership with Sarah Arnold, Colleen Bazinet, Maureen Cronin, Diana Garity, Griselda George, Pat O’Brien, Nora Studley, Dolores Theolien, and Lauren Woldemariam. The landforms investigation was developed in partnership with Cate Lacroix, Mary McCusker, Deborah Quinn, Melissa Richard, Traci Post, and Andrea Wells. The authors thank Rich Lehrer, Chris Georgen, and the three anonymous reviewers for their suggestions, which substantially improved the paper.

Funding

Funding is from the National Science Foundation, Grant 1749324.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eve Manz.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants/Informed Consent

Research and consent process were approved by the Boston University’s Institutional Review Board.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manz, E., Beckert, B. Quantification in Empirical Activity. Sci & Educ 32, 447–480 (2023). https://doi.org/10.1007/s11191-021-00301-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11191-021-00301-3

Keywords

Navigation