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Examining How Students Code with Socioscientific Data to Tell Stories About Climate Change

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Abstract

Data and computational literacies empower youth to be active participants and future leaders in our increasingly data-driven society. We conducted a design-based research project in which a small group (n = 5) of high school youth from diverse backgrounds learned how to code and create data visualizations and stories with public data about climate change in a 5-day (20 h total) free virtual summer program. Using interaction analysis methods to microanalyze students’ engagements with data technologies, we developed the computational data literacy model (CDLM) to describe students’ participation in various computational data literacies that emerged from our analysis (remixing, wayfinding, interpreting variables, making hypotheses, and personalizing data) and their use of different data tools (the code, data visualization, variable of interest, and story) to support scientific inquiry and reasoning. Using the CDLM, the presented analysis investigates how students navigated across coding and storytelling cycles of activities. Within those cycles, students collaboratively problem-solved in the code and engaged in collaborative inquiry, drawing on personal experiences to make multivariate hypotheses and stories about human impacts on carbon emissions. Our findings suggest that using a socioscientific issue (SSI) context supported students’ back-and-forth movement between coding and storytelling activities, perhaps by affording greater personalization of the data, which, in turn, facilitated data-based reasoning. The findings of this study inform our understanding of the challenges and learning opportunities in this computational, data-rich intervention situated in socioscientific inquiry. We discuss future uses of our model for learning designs to support computational data activities about SSIs.

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Data Availability

The data supporting this study's findings are available upon request from the corresponding author. Due to privacy or ethical restrictions, some data may not be publicly available. Requests for access to the data should be addressed to the corresponding author at hrsanei@ncsu.edu

References

Author references are blinded for review

  • Amanullah, K., & Bell, T. (2019). Evaluating the use of remixing in scratch projects based on repertoire, lines of code (loc), and elementary patterns. In 2019 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE.

  • Ames, M. G. (2013). Managing mobile multitasking: The culture of iPhones on Stanford campus. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work, 1487–1498. https://doi.org/10.1145/2441776.2441945

  • Bargagliotti, A., Franklin, C. A., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. (2020). Pre-K–12 guidelines for assessment and instruction in statistics education II (GAISEII): A framework for statistics and data science education, American Statistical Association., Alexandria, VA. https://www.amstat.org/education/guidelinesfor-assessment-and-instruction-in-statistics-education-(gaise)-reports

  • Bondaryk, L. G., Hsi, S., & Van Doren, S. (2021). Probeware for the modern era: IoT dataflow system design for secondary classrooms. IEEE Transactions on Learning Technologies, 14(2), 226–237.

    Article  Google Scholar 

  • Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013). What makes a visualization memorable? IEEE Transactions on Visualization and Computer Graphics, 19(12), 2306–2315.

    Article  PubMed  Google Scholar 

  • Börner, K., Maltese, A., Balliet, R. N., & Heimlich, J. (2016). Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors. Information Visualization, 15(3), 198–213.

    Article  Google Scholar 

  • Boy, J., Rensink, R. A., Bertini, E., & Fekete, J. D. (2014). A principled way of assessing visualization literacy. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1963–1972.

    Article  PubMed  Google Scholar 

  • Bruckman, A., & DeBonte, A. (1997). MOOSE goes to school: A comparison of three classrooms using a CSCL environment. Proceedings of the Computer-Supported Collaborative Learning Conference, 20–26.

  • Cairo, A. (2012). The functional art: An introduction to information graphics and visualization. New Riders.

  • Cobb, P., Confrey, J., DiSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.

    Article  Google Scholar 

  • Dasgupta, S., Hale, W., Monroy-Hernández, A., & Hill, B. M. (2016). Remixing as a pathway to computational thinking. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 438–1449. https://doi.org/10.1145/2818048.2819984

  • Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296.

    Article  Google Scholar 

  • D'ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

  • Erickson, T., Wilkerson, M., Finzer, W., & Reichsman, F. (2019). Data moves. Technology Innovations in Statistics Education, 12(1). https://doi.org/10.5070/T5121038001

  • Finzer, W., Busey, A., & Kochevar, R. (2018). Data-driven inquiry in the PBL classroom. Science Teacher, 86(1), 28–34. Retrieved July 29, 2022, from https://www.learntechlib.org/p/189591/

  • Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report: A pre-K--12 curriculum framework. Alexandria, VA: American Statistical Association. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEPreK-12_Full.pdf 

  • Friel, S., O’Connor, W., & Mamer, J. (2006). More than “meanmedianmode” and a bar graph: What’s needed to have a statistical conversation? In G. Burrill & P. Elliott (Eds.), Thinking and reasoning with data and chance: Sixty-eighth Yearbook (pp. 117–137). National Council of Teachers of Mathematics.

    Google Scholar 

  • Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics, 43, S182–S189. https://doi.org/10.1111/test.12283

    Article  Google Scholar 

  • Gately, C., Hutyra, L. R., & Wing, I. S. (2019). DARTE annual on-road CO2 emissions on a 1-km grid, Conterminous USA, V2, 1980–2017. ORNL DAAC.

  • Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://cacm.acm.org/magazines/2021/12/256932-datasheets-for-datasets/fulltext

  • Gould, R., Machado, S., Ong, C., Johnson, T., Molyneux, J., Nolen, S., Tangmunarunkit, H., Trusela, L., & Zanontian, L. (2016). Teaching data science to secondary students – the mobilize introduction to data science curriculum. In: J. Engel (Ed.), Promoting understanding of statistics about society. Proceedings of the IASE Roundtable Conference. https://iase-web.org/Conference_Proceedings.php

  • Hall, R. (1999). The organization and development of discursive practices for “having a theory.” Discourse Processes, 27(2), 187–218.

    Article  Google Scholar 

  • Hardy, L., Dixon, C., & Hsi, S. (2020). From data collectors to data producers: Shifting students’ relationship to data. Journal of the Learning Sciences, 29(1), 104–126.

    Article  Google Scholar 

  • Headrick Taylor, K. (2017). Learning along lines: Locative literacies for reading and writing the city. Journal of the Learning Sciences, 26(4), 533–574.

    Article  Google Scholar 

  • Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. Assessment and Teaching of 21st Century Skills: Methods and Approach, 37–56.

  • Kahn, J., & Jiang, S. (2020). Learning with large, complex data and visualizations: youth data wrangling in modeling family migration. Learning, Media, and Technology. https://doi.org/10.1080/17439884.2020.1826962

    Article  Google Scholar 

  • You, S., & Thompson, C. K. (2017). Mobile collaborative mixed reality for supporting scientific inquiry and visualization of earth science data. In 2017 IEEE Virtual Reality (VR) (241–242). IEEE.

  • Jiang, S., & Kahn, J. (2020). Data wrangling practices and collaborative interactions with aggregated data. International Journal of Computer-Supported Collaborative Learning, 15, 257–281. https://doi.org/10.1007/s11412-020-09327-1

    Article  Google Scholar 

  • Jiang, S., Nocera, A., Tatar, C., Yoder, M. M., Chao, J., Wiedemann, K., Finzer, W., & Rosé, C. P. (2022). An empirical analysis of high school students’ practices of modelling with unstructured data. British Journal of Educational Technology, 53(5), 1114–1133.

    Article  Google Scholar 

  • Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39–103.

    Article  Google Scholar 

  • Zeidler, D. L. (2014). Socioscientific issues as a curriculum emphasis: Theory, research and practice. In S. K. Abell & N. G. Lederman (Eds.), Handbook of Research on Science Education, 697–726.

  • Kahn, J., Herbel-Eisenmann, B., Jiang, S., Lim, V. Y., Peralta, L. M., & Rubel, L. H. (2022). Notice, wonder, feel, act, and reimagine: A framework for social justice and equity in data science education. Educational Technology and Society, 25(4). https://www.j-ets.net/collection/publishedissues/25_4

  • Kafai, Y. B. (2006). Playing and making games for learning: Instructionist and constructionist perspectives for game studies. Games and Culture, 1(1), 36–40.

    Article  Google Scholar 

  • Kafai, Y. B., & Proctor, C. (2022). A revaluation of computational thinking in K–12 education: Moving toward computational literacies. Educational Researcher, 51(2), 146–151.

    Article  Google Scholar 

  • K-12 Computer Science Framework Steering Committee. (2016). K–12 computer science framework. Retrieved from https://k12cs.org

  • Lanouette, K. (2022). Emotion, place, and practice: Exploring the interplay in children’s engagement in ecologists’ sampling practices. Science Education, 106(3), 610–644.

    Article  ADS  Google Scholar 

  • Lee, C. H., & Soep, E. (2016). None but ourselves can free our minds: Critical computational literacy as a pedagogy of resistance. Equity & Excellence in Education, 49(4), 480–492.

    Article  Google Scholar 

  • Lee, H., Mojica, G., Thrasher, E., & Baumgartner, P. (2022). Investigating data like a data scientist: Key practices and processes. Statistics Education Research Journal, 21(2), 3–3.

    Article  Google Scholar 

  • Lee, H. S., & Tran, D. (2015). Framework for supporting students’ approaches to statistical investigations: A guiding framework for the teaching statistics through data investigations. Teaching Statistics Through Data Investigation MOOC-Ed. Friday Institute for Educational Innovation, NC State University. https://s3.amazonaws.com/fi-courses/tsdi/unit_3/SASI/20Framework.pdf

  • 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.

    Article  ADS  Google Scholar 

  • Lee, S., Kwon, B. C., Yang, J., Lee, B. C., & Kim, S. H. (2019). The correlation between users’ cognitive characteristics and visualization literacy. Applied Sciences, 9(3), 488.

    Article  Google Scholar 

  • Lee, V. R., & Wilkerson, M. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects [Commissioned Paper for the National Academies of Sciences, Engineering, and Medicine, Board on Science Education, Committee on Science Investigations and Engineering Design for Grades 6–12]. National Academies of Sciences, Engineering, and Medicine.

  • Lee, V. R., Wilkerson, M. H., & Lanouette, K. (2021). A call for a humanistic stance toward K–12 data science education. Educational Researcher, 50(9), 664–672.

    Article  CAS  Google Scholar 

  • Li, K., Uvah, J., Amin, R., & Okafor, A. (2010). A study of college readiness for college algebra. Journal of Mathematical Sciences & Mathematics Education, 5(1), 52–66.

    Google Scholar 

  • Lopez, M. L., Roberto, C., Rivero, E., Wilkerson, M. H., Bakal, M., & Gutiérrez, K. (2021). Curricular reorganization in the third space: A case of consequential reasoning around data. In de Vries E., Hod Y., Ahn J. (Eds.). Proceedings of the 2021 Annual Meeting of the International Society for the Learning Sciences, [ISLS 2021], 1, 466–473. International Society of the Learning Sciences.

  • Lytle, N., Cateté, V., Boulden, D., Dong, Y., Houchins, J., Milliken, A., Isvik, A., Bounajim, D., Wiebe, E., & Barnes, T. (2019). Use, modify, create: Comparing computational thinking lesson progressions for stem classes. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, 395–401.

  • Mayes, R., Long, T., Huffling, L., Reedy, A., & Williamson, B. (2020). Undergraduate quantitative biology impact on biology preservice teachers. Bulletin of Mathematical Biology, 82, 1–28. https://doi.org/10.1007/s11538-020-00740-z

    Article  Google Scholar 

  • Marin, A., & Bang, M. (2015). Designing pedagogies for indigenous science education: Finding our way to storywork. Journal of American Indian Education, 54(2), 29–51.

    Article  Google Scholar 

  • Murray, S. (2017). Interactive data visualization for the web: An introduction to designing with D3. "O'Reilly Media, Inc."

  • Nair, L., Shetty, S., & Shetty, S. (2016). Interactive visual analytics on big data: Tableau vs D3. js. Journal of e-Learning and Knowledge Society, 12(4).

  • Namdar, B., & Shen, J. (2018). Knowledge organization through multiple representations in a computer-supported collaborative learning environment. Interactive Learning Environments, 26(5), 638–653. https://doi.org/10.1080/10494820.2017.1376337

    Article  Google Scholar 

  • Ochs, E., Taylor, C., Rudolph, D., & Smith, R. (1992). Storytelling as a theory-building activity. Discourse Processes, 15(1), 37–72.

    Article  Google Scholar 

  • Pangrazio, L., & Sefton-Green, J. (2020). The social utility of ‘data literacy.’ Learning, Media and Technology, 45(2), 208–220.

    Article  Google Scholar 

  • Peppler, K. A., & Kafai, Y. B. (2007). From SuperGoo to Scratch: Exploring creative digital media production in informal learning. Learning, Media, and Technology, 32, 149–166.

    Article  Google Scholar 

  • Philip, T. M., Schuler-Brown, S., & Way, W. (2013). A framework for learning about big data with mobile technologies for democratic participation: Possibilities, limitations, and unanticipated obstacles. Technology, Knowledge and Learning, 18(3), 103–120.

    Article  Google Scholar 

  • Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701.

    Article  Google Scholar 

  • Rosenberg, J. M., Schultheis, E. H., Kjelvik, M. K., Reedy, A., & Sultana, O. (2022). Big data, big changes? The technologies and sources of data used in science classrooms. British Journal of Educational Technology, 53(1), 174–190. https://doi.org/10.1111/bjet.13198

    Article  Google Scholar 

  • Ryan, L. (2016). The visual imperative: Creating a visual culture of data discovery. Morgan Kaufmann.

  • Rubel, L. H., Hall-Wieckert, M., & Lim, V. Y. (2017). Making space for place: Mapping tools and practices to teach for spatial justice. Journal of the Learning Sciences, 26(4), 643–687.

    Article  Google Scholar 

  • Rubel, L. H., Nicol, C., & Chronaki, A. (2021). A critical mathematics perspective on reading data visualizations: Reimagining through reformatting, reframing, and renarrating. Educational Studies in Mathematics, 108(1), 249–268.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sadler, T. D. (2011). Socio-scientific issues-based education: What we know about science education in the context of SSI. In: Sadler, T. (eds) Socio-scientific Issues in the Classroom. Contemporary Trends and Issues in Science Education, 39. https://doi.org/10.1007/978-94-007-1159-4_20

  • Salimpour, S., Fitzgerald, M. T., Tytler, R., & Eriksson, U. (2021). Educational design framework for a web-based interface to visualise authentic cosmological “big data” in high school. Journal of Science Education and Technology, 30(5), 732–750.

    Article  Google Scholar 

  • Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–1148.

    Article  PubMed  Google Scholar 

  • Stornaiuolo, A. (2020). Authoring data stories in a media makerspace: Adolescents developing critical data literacies. Journal of the Learning Sciences, 29(1), 81–103.

    Article  Google Scholar 

  • Strauss, A. L. (1987). Qualitative analysis for social scientists. Cambridge University Press.

    Book  Google Scholar 

  • Sung, S. H., Li, C., Chen, G., Huang, X., Xie, C., Massicotte, J., & Shen, J. (2021). How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct. Journal of Science Education and Technology, 30(2), 210–226.

    Article  ADS  Google Scholar 

  • Wang, C., Shen, J., & Chao, J. (2021). 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  ADS  Google Scholar 

  • Vakil, S. (2014). A critical pedagogy approach for engaging urban youth in mobile app development in an afterschool program. Equity & Excellence in Education, 47(1), 31–45.

    Article  Google Scholar 

  • Vogelstein, L., Brady, C., & Hall, R. (2019). Reenacting mathematical concepts found in large-scale dance performance can provide both material and method for ensemble learning. ZDM Mathematics Education, 51(2), 331–346.

    Article  Google Scholar 

  • Watson, J., Fitzallen, N., Fielding-Wells, J., & Madden, S. (2018). The practice of statistics. In Ben-Zvi, D., Makar, K., & Garfield, J. (Eds.), International handbook of research in Statistics Education, 105–137. Springer. https://doi.org/10.1007/978-3-319-66195-7_4

  • Wertsch, J. (1998). Mind as action. Oxford University Press.

    Google Scholar 

  • Wilkerson, M. H., & Laina, V. (2018). Middle school students’ reasoning about data and context through storytelling with repurposed local data. ZDM Mathematics Education, 50(7), 1223–1235. https://doi.org/10.1007/s11858-018-0974-9

    Article  Google Scholar 

  • Yalcinkaya, R., Sanei, H., Wang, C., Zhu, Li., Kahn, J., & Jiang, S. (2022). Remixing as a key practice for coding and data storytelling. Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning-CSCL 2022. Hiroshima, Japan. https://www.dropbox.com/s/9mwx6t8mi75op15/CSCL2022%20Proceedings.pdf?dl=0

  • Wilkerson, M. H., & Polman, J. L. (2020). Situating data science: Exploring how relationships to data shape learning [Special issue]. Journal of the Learning Sciences, 29(1), 1–10.

    Article  Google Scholar 

  • Xie, C., Li, C., Sung, S., & Jiang, R. (2022). Engaging students in distance learning of science with remote labs 2.0. IEEE Transactions on Learning Technologies, 15(1), 15–31.

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Acknowledgements

We thank Alberto Cairo for supporting and advising this project.

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Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, USA

    Hamid Sanei, Rabia Yalcinkaya & Shiyan Jiang

  2. University of Miami, Coral Gables, FL, USA

    Jennifer B. Kahn & Changzhao Wang

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  1. Hamid Sanei
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  2. Jennifer B. Kahn
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  4. Shiyan Jiang
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Contributions

The manuscript was jointly written by the author team. The first author led the data analysis and interpretations of the results from the data analysis. The second author led the data collection and contributed to the data analysis and interpretations. The third author led the literature review and contributed to the data analysis and interpretations. The project was conceived and advised by the last two authors.

Corresponding author

Correspondence to Hamid Sanei.

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Ethics Approval and Consent to Participate

Informed consent was obtained from all individual participants included in the study. Students signed informed consent regarding publishing their data and photographs. The participant consented to submit the case report to the journal.

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Appendix. Interview protocol

Appendix. Interview protocol

Introduction

During our sessions, you learned about data visualization and its components, and finally, you built a data story and visualization that you were interested in. I want to mention that you did a great job and I hope you also had some fun over this experience. Now, let’s talk about your overall experience. We are going to record this interview, is it okay with you?

Now, let’s take a look at the data story and visualization that you created.

Designing and building data visualizations process:

  1. 1.

    Can you tell us your data story?

  2. 2.

    Describe for us step-by-step what your overall process was for creating your data visualization project. What did you do first, second, third, last?

  3. 3.

    Where did the ideas come from for creating this data visualization project?

  4. 4.

    What kind of challenges did you encounter when creating the project and how did you address it? (e.g., coding, making selections for your data display)

Learning about storytelling with data:

  1. 1.

    Did you feel personally connected to the data (or story told)? How so?

  2. 2.

    Were there other stories you could have told with the data? Any counter-stories?

  3. 3.

    Do you think you will tell stories with data in the future? What might they be about?

General feedback:

  1. 1.

    What do you think you learned from creating this project?

  2. 2.

    If you had more time to work on your data visualization project, what would you add or change? Why? What do you want to do in the Fall Studio?

  3. 3.

    What is your favorite part of this week? Why?

  4. 4.

    What did you enjoy least about the project? Any suggestions on improving it? (Or something you would like to do in the Fall Studio?)

  5. 5.

    Anything else you’d like for us to know?

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Sanei, H., Kahn, J.B., Yalcinkaya, R. et al. Examining How Students Code with Socioscientific Data to Tell Stories About Climate Change. J Sci Educ Technol 33, 161–177 (2024). https://doi.org/10.1007/s10956-023-10054-z

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