Skip to main content
SpringerLink
Log in

Integrating Computational Thinking into Geoscientific Inquiry About Volcanic Eruption Hazards and Risks

  • Published:
International Journal of Science and Mathematics Education Aims and scope Submit manuscript

Abstract

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427. https://doi.org/10.1080/0020739X.2016.1254296

    Article  Google Scholar 

  • Bonadonna, C. (2005). Probabilistic modeling of tephra dispersal: Hazard assessment of a multiphase rhyolitic eruption at Tarawera, New Zealand. Journal of Geophysical Research, 110(B3), B03203. https://doi.org/10.1029/2003JB002896

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at the 2012 Annual Meeting of the American Educational Research Association, Vancouver Canada.

  • Brown, J., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.

    Article  Google Scholar 

  • Chen, Z., & Klahr, D. (1999). All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development, 70(5), 1098–1120.

    Article  Google Scholar 

  • Connor, C., Bebbington, M., & Marzocchi, W. (2015). Chapter 51—Probabilistic volcanic hazard assessment. In H. Sigurdsson (Ed.), The encyclopedia of volcanoes (2nd ed.). Academic Press. https://doi.org/10.1016/B978-0-12-385938-9.00051-1

    Chapter  Google Scholar 

  • Denning, P. (2007). Computing is a natural science. Communications of the ACM, 50, 13–18.

    Article  Google Scholar 

  • diSessa, A. (2001). Changing minds: Computers, learning, and literacy. MIT Press.

    Google Scholar 

  • Dunbar, K. (1995). How scientists really reason: Scientific reasoning in real-world laboratories. In R. J. Sternberg & J. E. Davidson (Eds.), The Nature of Insight (pp. 365–395). The MIT Press.

    Google Scholar 

  • Fennell, H. W., Lyon, J. A., Madamanchi, A., & Magana, A. J. (2020). Toward computational apprenticeship: Bringing a constructivist agenda to computational pedagogy. Journal of Engineering Education, 109(2), 170–176. https://doi.org/10.1002/jee.20316

    Article  Google Scholar 

  • Froyd, J. E., Wankat, P. C., & Smith, K. A. (2012). Five major shifts in 100 years of engineering education. In Proceedings of the IEEE (Vol. 100, Special Centennial Issue, pp. 1344-1360). IEEE.

    Article  Google Scholar 

  • Giere, R. N. (1988). Explaining Science: A cognitive approach. University of Chicago Press.

    Book  Google Scholar 

  • Graham, M., Milanowski, A., & Jackson, M. (2012). Measuring and promoting inter-rater agreement of teacher and principal performance ratings. Center for Educator Compensation Reform.

    Google Scholar 

  • Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–43.

    Article  Google Scholar 

  • Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.

    Article  Google Scholar 

  • Gweon, G.-H. & Lee, H.-S. (2022). Applying deweyan perspective of inquiry to teaching experimentation using simulation. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1281-1284). International Society of the Learning Sciences.

    Google Scholar 

  • Hankinson-Nelson, L. (1990). Who knows: From Quine to feminist empiricism. Temple University Press.

    Google Scholar 

  • Henderson, P. B., Cortina, T. J., Hazzan, O., & Wing, J. M. (2007). Computational thinking. In I. Russell & S. Haller (Eds.), Proceedings of the 38th ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’07) (pp. 195–196). Association for Computing Machinery. https://doi.org/10.1145/1227310.1227378

    Chapter  Google Scholar 

  • Hurst, T., & Smith, W. (2004). A Monte Carlo methodology for modelling ashfall hazards. Journal of Volcanology and Geothermal Research, 138(3), 393–403.

    Article  Google Scholar 

  • Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., & Kumar, V. (2019). Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1544.

    Article  Google Scholar 

  • Kastens, K. A., Manduca, C. A., Cervato, C., Frodeman, R., Goodwin, C., Liben, L. S., Mogk, D. W., Spangler, T. C., Stillings, N. A., & Titus, S. (2009). How geoscientists think and learn. Eos, Transactions American Geophysical Union, 90(31), 265–266.

    Article  Google Scholar 

  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

    Book  Google Scholar 

  • Lee, H.-S., & Songer, N. B. (2003). Making authentic science accessible to students. International Journal of Science Education, 25(8), 923–948.

    Article  Google Scholar 

  • Lee, I., Grover, S., Martin, F., Pillai, S., & Malyn-Smith, J. (2020). Computational thinking from a disciplinary perspective: Integrating computational thinking in K-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology, 29(1), 1–8. https://doi.org/10.1007/s10956-019-09803-w

    Article  Google Scholar 

  • Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3(2), 147–166. https://doi.org/10.1007/s41979-020-00044-w

    Article  Google Scholar 

  • Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012

    Article  Google Scholar 

  • Manduca, C. A., & Kastens, K. A. (2012). Mapping the domain of spatial thinking in the geosciences. Geological Society of America Special Papers, 486, 45–49.

    Google Scholar 

  • Medunic, G., Chakravarty, S., & Kundu, R. (2022, July). Computational skills in geosciences higher education system for the 21st century. Paper presented at the 4th Croatian Scientific Congress about Geomathematics and Terminology in Geology, Zagreb, Croatia.

    Google Scholar 

  • Miller, E., Manz, E., Russ, R., Stroupe, D., & Berland, L. (2018). Addressing the epistemic elephant in the room: Epistemic agency and the next generation science standards. Journal of Research in Science Teaching, 55(7), 1053–1075.

    Article  Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas (p. 13165). National Academies Press. https://doi.org/10.17226/13165

    Book  Google Scholar 

  • Newhall, C. G., & Self, S. (1982). The volcanic explosivity index (VEI) an estimate of explosive magnitude for historical volcanism. Journal of Geophysical Research: Oceans, 87(C2), 1231–1238. https://doi.org/10.1029/JC087iC02p01231

    Article  Google Scholar 

  • NGSS Lead States. (2013). The next generation science standards. The National Academies Press.

    Google Scholar 

  • Ogegbo, A. A., & Ramnarain, U. (2022). A systematic review of computational thinking in science classrooms. Studies in Science Education, 58(2), 203–230.

    Google Scholar 

  • Orton, K., Weintrop, D., Beheshti, E., Horn, M., Jona, K., & Wilensky, U. (2016). Bringing computational thinking into high school mathematics and science classrooms, 2, 705–712.

  • Pallant, A., Lee, H.-S., & Pryputniewicz, S. (2012). Students learn how adjusting variables in a dynamic model affects the entire system. The Science Teacher, 79(7), 39–42.

    Google Scholar 

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.

    Google Scholar 

  • Quintana, C., Reiser, B. J., Davis, E., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson, D., & Soloway, E. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337–386.

    Article  Google Scholar 

  • Resnick, M., Maloney, J., Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch: Programming for everyone. Communications of the ACM, 52, 60–67.

    Article  Google Scholar 

  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172.

    Article  Google Scholar 

  • Shiflet, A. B., & Shiflet, G. W. (2014). Introduction to computational science: Modeling and simulation for the sciences. Princeton University Press.

    Google Scholar 

  • Stieff, M., & Wilensky, U. (2003). Connected chemistry—Incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12(3), 285–302. https://doi.org/10.1023/A:1025085023936

    Article  Google Scholar 

  • Stroupe, D. (2014). Examining classroom science practice communities: How teachers and students negotiate epistemic agency and learn science-as-practice. Science Education, 98(3), 487–516.

    Article  Google Scholar 

  • Stroupe, D. (2015). Describing “science practice” in learning settings. Science Education, 99(6), 1033–1040. https://doi.org/10.1002/sce.21191

    Article  Google Scholar 

  • Tinker, R. F., & Xie, Q. (2008). Applying computational science to education: The molecular workbench paradigm. Computing in Science and Engineering, 10(5), 24–27.

    Article  Google Scholar 

  • Uzzo, S., & Chen, R. (2015). Integrating computational thinking and environmental science: Design based research on using simulated ecosystems to improve student understanding of complex system behavior.

    Google Scholar 

  • Wang, C., Shen, J., & Chao, J. (2022). Integrating Computational Thinking in STEM Education: A Literature Review. International Journal of Science and Mathematics Education, 20(8), 1949–1972. https://doi.org/10.1007/s10763-021-10227-5

    Article  Google Scholar 

  • Waterman, K., Goldsmith, L., Pasquale, M., Goldenberg, P., Malyn-Smith, J., DeMallie, A., & Lee, I. (2018). Integrating computational thinking into elementary mathematics and science curriculum materials and instruction. Conference Proceedings. The Future of Education, 2018 https://conference.pixel-online.net/library_scheda.php?id_abs=3076

  • Waterman, K. P., Goldsmith, L., & Pasquale, M. (2020). Integrating computational thinking into elementary science curriculum: An examination of activities that support students’ computational thinking in the service of disciplinary learning. Journal of Science Education and Technology, 29(1), 53–64.

    Article  Google Scholar 

  • Weintrop, D., Beheshti, E., Horn, M., et al. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25, 127–147.

    Article  Google Scholar 

  • Wilensky, U., & Stroup, W. (2000). Networked gridlock: Students enacting complex dynamic phenomena with the HubNet architecture. In B. Fishman & S. O'Connor-Divelbiss (Eds.), Fourth International Conference of the Learning Sciences (pp. 282-289). Erlbaum.

  • Wilkerson, M. H., & Fenwick, M. (2017). The practice of using mathematics and computational thinking. In C. V. Schwarz, C. Passmore, & B. J. Reiser (Eds.), Helping students make sense of the world using next generation science and engineering practices (pp. 181–204). National Science Teachers’ Association Press.

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

Download references

Disclosure

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Funding

This material is based on work supported by the National Science Foundation under grant no. DRL-1841928.

Author information

Authors and Affiliations

  1. The Concord Consortium, 25 Love Lane, Concord, MA, 01742, USA

    Christopher Lore, Hee-Sun Lee, Amy Pallant & Jie Chao

  2. University of South Florida, Tampa, USA

    Charles Connor

Authors
  1. Christopher Lore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hee-Sun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amy Pallant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charles Connor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher Lore.

Ethics declarations

Ethical Approval and Consent to Participate

This project was reviewed by Ethical & Independent Review Services. It is deemed that the proposed involvement of human subjects met the criteria for an exemption from further IRB approval beyond an initial assessment. Informed consent was obtained from the teachers who participated in the study.

Supplementary Information

ESM 1

(DOCX 279 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lore, C., Lee, HS., Pallant, A. et al. Integrating Computational Thinking into Geoscientific Inquiry About Volcanic Eruption Hazards and Risks. Int J of Sci and Math Educ (2023). https://doi.org/10.1007/s10763-023-10426-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10763-023-10426-2

Keywords

Search

Navigation