Journal of Research in Science, Mathematics and Technology Education

Learning Computational Thinking Practices Through Agent-Based Modeling in an Informal Setting

Journal of Research in Science, Mathematics and Technology Education, Volume 5, Issue SI, June 2022, pp. 17-39
OPEN ACCESS VIEWS: 785 DOWNLOADS: 523 Publication date: 15 Jun 2022
ABSTRACT
In this qualitative study, I investigated the form of interaction when learners co-constructed computational thinking practices in an informal setting. This study focuses on the roles of five youth, three adult mentors, and a facilitator during a summer camp and documents how learners interacted to exchange ideas, collaborate, and accommodate other perspectives. Participants used questions that promote deep thinking to engage in computational thinking practices during the interactions. Analysis of data shows that learners developed computational thinking practices including interpreting, modeling connections to the real-world, manipulating parameters, discovering ideas to explore models, prompting, conjecturing, and making predictions when working on agent-based modeling and writing codes in NetLogo. These practices are further grouped together as model interpretation and connection to the real world, parameter manipulation and discovery, prompting and exploring, and making predictions/conjectures and generalizing. This study also finds some community-based practices such as practical wisdom, trying something and adjusting, and the use of network of resources relevant during interaction to develop computational thinking practices.
KEYWORDS
Agent-Based Modeling, Computational Thinking, Informal Education, Modeling
CITATION (APA)
Adeolu, A. (2022). Learning Computational Thinking Practices Through Agent-Based Modeling in an Informal Setting. Journal of Research in Science, Mathematics and Technology Education, 5(SI), 17-39. https://doi.org/10.31756/jrsmte.112SI
REFERENCES
  1. Adams P. (2006) Exploring social constructivism: Theories and practicalities, Education 3-13,34:3, 243-257, DOI: 10.1080/03004270600898893
  2. Adeolu, A.S (2020). Practices of Facilitators when Planning Mathematical Modeling Activities in an Informal Setting. In A.I. Sacristán, J.C. Cortés-Zavala & P.M. Ruiz-Arias, (Eds.). Mathematics Education Across Cultures: Proceedings of the 42nd meeting of the North American Chapter of The International Group for the Psychology of Mathematics Education, Mexico (2027 - 2031).
  3. Angeli, C., Voogt C., Fluck A., Webb M., Cox M., Malyn-Smith J., & Zagami J. (2016). A K-6 Computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-57.
  4. Bentol, L., Hoyles, C., Kalas, I. et al. (2017) Bridging Primary Programming and Mathematics:
  5. Some Findings of Design Research in England. Digit Exp Math Educ 3, 115–138.
  6. Bliss, K.M, Galluzzo B.J., Kavanagh, K.R., & Levy, R. (2018). Math modeling: Computing & communicating. The Society for Industrial and Applied Mathematics.
  7. DeCuir-Gunby, Jessica & Marshall, Patricia & Mcculloch, Allison. (2011). Developing and using a codebook for the analysis of interview data: An example from a professional development research project. Field Methods - FIELD METHOD. 23. 136-155. DOI: 10.1177/1525822X10388468.
  8. Google: Exploring computational thinking. (n.d.). Retrieved 25 Oct 2010. http://www.google.com/edu/computational-thinking/ index.html
  9. Jari L, Ossi A, and Veijo M. (2004). Creative and collaborative problem solving in technology education: A case study in primary school teacher education. The Journal of Technology Studies.
  10. Johnson, D. W., & Johnson, R. T. (1999). Making cooperative learning work. Theory into Practice, 38, 67–DOI: 73.10.1080/00405849909543834
  11. Jordan, B & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences. 4. 39-103. 10.1207/s15327809jls0401_2.
  12. King H., Dillon J. (2012) Learning in Informal Settings. In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428 6_1101
  13. Lockwood, Elise. (2019). Using a computational context to investigate student reasoning about whether “order matters” in counting problems. Proceedings of the 22nd Annual Conference on Research in Undergraduate Mathematics Education.
  14. Lockwood, E., DeJarnette, A., & Thomas, M. (2019). Computing as a mathematical disciplinary practice. The Journal of Mathematical Behavior. 54. 10.1016/j.jmathb.2019.01.004.
  15. Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods
  16. sourcebook. Edition 3.
  17. Mouza A, Yadav C, & Ottenbreit-Leftwich A. (2021). Preparing pre-service teachers to teach computer science: Models, practices, and policies. Research, Innovation & Methods in Educational Technology. ISBN: 9781648024580
  18. National Governors Association Center for Best Practices & Council of Chief State School Officers. (2010). Common Core State Standards for Mathematics. Washington, DC.
  19. National Research Council. (2010). Committee for the workshops on computational thinking: Report of a workshop on the scope and nature of computational thinking. Washington, DC: National Academy Press. doi:10.17226/12840.
  20. Next Generation Science Standards Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
  21. Nicholls, S., Amelung, B., & Student, J. (2017). Agent-Based Modeling: A powerful tool for tourism researchers. Journal of Travel Research, 56(1), 3–15. https://doi.org/10.1177/0047287515620490
  22. Peck, F.A., Carlson, M.A., Adeolu, A.S., Killeen, S., & McWalters, K (2020). Problem solving dispositions in rural communities. Mathematics Education Across Cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, Mexico (pp. 1456).
  23. Rogoff, B., Callanan, M., Gutiérrez, K. D., & Erickson, F. (2016). The Organization of Informal Learning. Review of Research in Education, 40(1), 356–401. https://doi.org/10.3102/0091732X16680994
  24. Schütte M., Friesen RA., & Jung J. (2019) Interactional Analysis: A method for analysing mathematical learning processes in interactions. In: Kaiser G., Presmeg N. (eds) Compendium for Early Career Researchers in Mathematics Education. ICME-13 Monographs. Springer, Cham. https://doi.org/10.1007/978-3-030-15636-7_5
  25. Selby, C. (2015). Relationships: computational thinking, pedagogy of programming, and bloom’s taxonomy. In Proceedings of the Workshop in Primary and Secondary Computing Education on ZZZ (pp. 80–87). New York: ACM.
  26. Sobels J., Szili G., & Bass D. (2012) Using constructivist teaching tools to stimulate active learning in first year environmental management undergraduates, Planet, 25:1, 21-26, DOI: 10.11120/plan.2012.00250021
  27. Stacey K. (2006). What is mathematical thinking and why is it important? University of Melbourne, Australia
  28. Stephens M., Kadijevich D.M. (2019) Computational/Algorithmic thinking. In: Lerman S. (eds) Encyclopedia of Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-319-77487-9_100044-1
  29. Tisue, S., & Wilensky, U (2004). “NetLogo: Design and implementation of a multi-agent modeling environment”, SwarmFest, Ann Arbor.
  30. Tofade, T., Elsner, J., & Haines, S. (2013). Best practice strategies for effective use of questions as a teaching tool. American Journal of Pharmaceutical Education, 77(7), 155. https://doi.org/10.5688/ajpe777155
  31. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Massachusetts: Harvard University Press.
  32. Weintrop, D., Beheshti, E., Horn, M., Orton, K. Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science and Education Technology, 25, 127-147. Doi: 10.1007/s10956-015-0581-5.
  33. Wenger, E. (1998). Communities of practice: learning, meaning, and identity. New York: Cambridge University Press.
  34. Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
  35. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35
  36. Yadav A., Stephenson C., Hong H. (2017). Computational thinking for teacher education. Communications of the ACM, April 2017, Vol. 60 No. 4, Pages 55-62 10.1145/2994591
  37. Yadav A., Gretter S., Good J., McLean T. (2018) Computational thinking in teacher education. In: Rich P., Hodges C. (eds) Emerging Research, Practice, and Policy on Computational Thinking. Educational Communications and Technology: Issues and Innovations. Springer, Cham.
LICENSE
Creative Commons License