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Chimica nella Scuola

Università, formazione docenti, orientamento, PLS

Vol. 1 No. 1 (2024): Chimica nella Scuola n. 1 2024

Insegnare chimica fisica con dashboard sviluppate in Python

Submitted
10 April 2024
Published
10-04-2024

Abstract

Mathematical models and graphs are essential in chemistry and other scientific disciplines, but their complexity can hinder student understanding. Interactive applications, such as dashboards, can simplify complex data and enhance comprehension. Python and Plotly Dash are effective tools for creating custom dashboards tailored to specific teaching needs. These dashboards proved useful for students with limited mathematical background, and they can also support active learning strategies and develop cross-cutting skills like programming. However, their effectiveness depends on integration into the teaching process and teacher guidance.

References

  1. S. Van den Eynde, M. Goedhart, J. Deprez, M. De Cock, Role of graphs in blending physical and mathematical meaning of partial derivatives in the context of the heat equation, Int. J. of Sci. and Math. Educ., 2023, 21, 25-47 (https://doi.org/10.1007/s10763-021-10237-3).
  2. C. Rasmussen, K. Marrongelle, M. C. Borba, Research on calculus: What do we know and where do we need to go? ZDM Mathematics Education, 2014, 46(4), 507–515 (https://doi.org/10.1007/s11858-014-0615-x).
  3. L. Liu, Y. Ling, J. Yu, Q. Fu, Developing and evaluating an inquiry-based online course with a simulation program of complexometric titration, J. Chem. Educ., 2021, 98(5), 1636–1644 (https://doi.org/10.1021/acs.jchemed.0c01229).
  4. M. Ben Ouahi, M. Ait Hou, A. Bliya, T. Hassouni, E. M. Al Ibrahmi, The effect of using computer simulation on students’ performance in teaching
  5. and learning physics: Are there any gender and area gaps? Education Research International, 2021, 2021, ID 6646017 (https://doi.org/10.1155/2021/6646017).
  6. University of Colorado Boulder, PhET: Free online physics, chemistry, biology, earth science and math simulations, PhET Interactive Simulations (https://phet.colorado.edu/).
  7. Khan Academy, Khan Academy - Free online courses, lessons & practice (https://www.khanacademy.org/).
  8. L. Vaughan, Python tools for scientists: An introduction to using Anaconda, JupyterLab, and Python’s Scientific Libraries, No Starch Press, San Francisco, 2023.
  9. L. Grandell, M. Peltomäki, R.-J. Back, T. Salakoski, Why complicate things? Introducing programming in high school using Python, in Proceedings of the 8th Australasian Conference on Computing Education - Volume 52; ACE ’06; Australian Computer Society, Inc., AUS, 2006, pp 71–80.
  10. D. B. Jordaan, Board games in the computer science class to improve students’ knowledge of the Python programming language, in 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC); 2018; pp 1–5 (https://doi.org/10.1109/ICONIC.2018.8601207).
  11. A. Jayal, S. Lauria, A. Tucker, S. Swift, Python for teaching introductory programming: A quantitative evaluation, Innovation in Teaching and Learning in Information and Computer Sciences, 2011, 10(1), 86–90 (https://doi.org/10.11120/ital.2011.10010086).
  12. C. J. Weiss, A creative commons textbook for teaching scientific computing to chemistry students with Python and Jupyter Notebooks, J. Chem. Educ., 2021, 98(2), 489–494 (https://doi.org/10.1021/acs.jchemed.0c01071).
  13. S. Hossain, Visualization of bioinformatics data with dash bio, Proceedings of the 18th Python in Science Conference, 2019, 126–133 (https://doi.org/10.25080/Majora-7ddc1dd1-012).
  14. Plotly technologies, Collaborative data science (https://plot.ly).
  15. M. Grinberg, Flask web development: Developing web applications with Python, 2° Edizione, Oreilly & Associates Inc, Sebastopol, California, 2018.