Data farming for virtual school laboratories
- Authors: Patarakin Y.D.1, Yarmakhov B.B.2
-
Affiliations:
- National Research University “Higher School of Economics,”
- Moscow City University
- Issue: Vol 18, No 4 (2021)
- Pages: 347-359
- Section: DIGITAL EDUCATIONAL ENVIRONMENT
- URL: https://journals.rudn.ru/informatization-education/article/view/30229
- DOI: https://doi.org/10.22363/2312-8631-2021-18-4-347-359
Cite item
Full Text
Abstract
Problem and goal. Building statistical, mathematical, computational and research literacies in teaching school subjects is discussed in the article. The purpose is to develop a model for generating data for research experiments by students. Methodology. The Netlogo data generation and consecutive statistical data procession in CODAP and R programming language were used. Results. The generative approach helps students to work with data collected by agents, programmed by students themselves. In doing so, the student assumes the position of a researcher, who plans an experiment and analyses its results. Conclusion. The proposed approach of data generation and analysis allows to introduce the student to the contemporary culture of generating and sharing data.
About the authors
Yevgeny D. Patarakin
National Research University “Higher School of Economics,”
Email: epatarakin@hse.ru
ORCID iD: 0000-0002-1216-5043
Doctor of Pedagogical Sciences, Academic Supervisor of the educational program “Digital Transformation of Education”
16/9 Potapovsky Pereulok, Moscow, 101000, Russian FederationBoris B. Yarmakhov
Moscow City University
Author for correspondence.
Email: yarmakhovbb@mgpu.ru
ORCID iD: 0000-0001-6217-0871
Candidate of Philosophical Sciences, Research Supervisor of the Center for Data Analysis, Institute for Digital Education
28 Sheremetyevskaya St, Moscow, 127521, Russian FederationReferences
- Ben-Zvi D, Makar K, Garfield J. International handbook of research in statistics education. Cham: Springer International Publishing; 2018.
- Erickson T, Finzer B, Reichsman F, Wilkerson M. Data moves: one key to data science at school level. Proceedings of the International Conference on Teaching Statistics (ICOTS-10). 2018;6:1-6.
- Fekete A, Kay J, Röhm U. A data-centric computing curriculum for a data science major. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. New York: Association for Computing Machinery; 2021. p. 865-871. https://doi.org/10.1145/3408877.3432457
- Bates M., Usiskin Z. Digital curricula in school mathematics. IAP; 2016.
- Wilkerson M, Lanouette K, Shareff R, St Clair N, Bulalacao N, Erickson T, Heller J, Finzer W, Reichsman F. Data transformations: restructuring data for inquiry in a simulation and data analysis environment. In Kay J, Luckin R. (eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count: 13th International Conference of the Learning Sciences 2018 (vol. 3). London: International Society of the Learning Sciences; 2018.
- Gibson P, Mourad T. The growing importance of data literacy in life science education. American Journal of Botany. 2018;105(12):1953-1956. https://doi.org/10.1002/ajb2.1195
- Uzunalioglu H, Cao J, Phadke C, Lehmann G, Akyamac A, He R, Lee J, Able M. Augmented data science: towards industrialization and democratization of data science. 2019. Available from: http://arxiv.org/abs/1909.05682 (accessed: 21.03.2021).
- Wilkerson MH. DataSketch: a tool to turn student sketches into data-driven visualizations. Frontiers in Pen and Touch. Springer; 2017. p. 227-234.
- Blikstein P. Seymour Papert’s legacy: thinking about learning, and learning about thinking. Seymour Papert Tribute at IDC 2013 (New York, 24-27 June 2013). New York; 2013.
- Mokros JR, Tinker RF. The impact of microcomputer-based labs on children’s ability to interpret graphs. Journal of Research in Science Teaching. 1987;24(4):369-383. https://doi.org/10.1002/tea.3660240408
- Tinker R, Krajcik JS. Portable technologies: science learning in context. London: Springer; 2002.
- Klopfer E. Augmented learning: research and design of mobile educational games. The MIT Press; 2008.
- Klopfer E, Sheldon J, Perry J, Chen VH-H. Ubiquitous games for learning (UbiqGames): Weatherlings, a worked example. J. Comp. Assist. Learn. 2012;28(5):465-476. https://doi.org/10.1111/j.1365-2729.2011.00456.x
- Patarakin ED. Wikigrams-based social inquiry. Digital Tools and Solutions for Inquiry-Based STEM Learning. 2017;1:112-138.
- Vachkova S, Petryaeva E, Patarakin E. Typology of schools operating in the Moscow Electronic School system based on the analysis of network indicators. SHS Web Conf. EDP Sciences. 2021;98:03001. https://doi.org/10.1051/shsconf/20219803001
- Bondaryk L, Hsi S, Van Doren S. Probeware for the modern era: IoT dataflow system design for secondary classrooms. IEEE Transactions on Learning Technologies. 2021;14(2):226-237. https://doi.org/10.1109/TLT.2021.3061040
- Dixon C, Hardy L, Hsi S, Van Doren S. Computational tinkering in science: designing space for computational participation in high school biology. International Society of the Learning Sciences; 2020.
- Tho SW, Yeung YY, Wei R, Chan KW, So WW. A systematic review of remote laboratory work in science education with the support of visualizing its structure through the HistCite and CiteSpace software. Int. J. of Sci. and Math. Educ. 2017;15(7):1217-1236. https://doi.org/10.1007/s10763-016-9740-z
- Hossain Z, Bumbacher E, Brauneis A, Diaz M, Saltarelli A, Blikstein P, Riedel-Kruse IH. Design guidelines and empirical case study for scaling authentic inquiry-based science learning via open online courses and interactive biology cloud labs. Int. J. Artif. Intell. Educ. 2018;28(4):478-507. https://doi.org/10.1007/s40593-017-0150-3
- De Caux R. An agent-based approach to modelling long-term systemic risk in networks of interacting banks (Doctoral Thesis). University of Southampton; 2017.
- Sayama H, Cramer C, Sheetz L, Uzzo S. NetSciEd: network science and education for the interconnected world. 2017. Available from: http://arxiv.org/abs/1706.00115 (accessed: 20.10.2020).
- Secchi D, Neumann M. (eds.) Agent-based simulation of organizational behavior. Cham: Springer International Publishing; 2016.
- Horne GE, Schwierz K-P. Data farming around the world overview. Proceedings of the 40th Conference on Winter Simulation. Miami; 2008. p. 1442-1447.
- Sanchez SM. Data farming: methods for the present, opportunities for the future. ACM Trans. Model. Comput. Simul. 2020;30(4):22:1-22:30. https://doi.org/10.1145/3425398
- Sanchez S. Data farming: better data, not just big data. 2018 Winter Simulation Conference. 2018. p. 425-439. https://doi.org/10.1109/WSC.2018.8632383
- Lorig F, Timm IJ. Simulation-based data acquisition. In: Arabnia HR, Daimi K, Stahlbock R, Soviany C, Heilig L, Brüssau K. (eds.) Principles of Data Science. Cham: Springer International Publishing; 2020. p. 1-15. https://doi.org/10.1007/978-3-030-43981-1_1
- Rakić K, Rosić M, Boljat I. A survey of agent-based modelling and simulation tools for educational purpose. Tehnički Vjesnik. 2020;27(3):1014-1020. https://doi.org/10.17559/TV-20190517110455
- Railsback SF, Grimm V. Agent-based and individual-based modeling: a practical introduction. 2nd ed. Princeton University Press; 2019.
- Wilensky U, Rand W. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press; 2015.