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Visualization Techniques for Analyzing Learning Effects – Taking Python as an Example

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Multimedia Technology and Enhanced Learning (ICMTEL 2023)

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Abstract

With the advent of the information age, data visualization technology has gradually shown its unique features in various information fields, and its importance has gradually been attached importance by various governments and commercial departments. In teaching in our country, most schools simply use office software to create pie chart, histogram or table to realize visualization. This kind of teaching creates a single chart that doesn’t change at all. The content of the data visualization course of many school courses is outdated, and the way of visualization is not in line with the needs of The Times, that is, the content has been criticized as abstract, language mechanization, format and public culture [1]. In order to analyze the teaching quality and evaluate and improve it, this paper processes and analyzes the data exported from the teaching administration system based on python, mainly from three aspects: data acquisition, data processing and data analysis. Firstly, the python crawler technology is used to obtain students’ grades, secondly, the invalid data is processed, and finally, the matplotlib library is used to visualize the processed data, and the learning status of students in the class is analyzed and evaluated by combining the obtained images. Through the data processing of this paper, it realizes the hiding of the student’s name, protects the privacy of the student, and uses the graph to intuitively reflect the student’s grade distribution, which makes the grade analysis more convenient.

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References

  1. Fabry, D.L., Higgs, J.R.: Barriers to the effective use of technology in education: current status. J. Educ. Comput. Res. 17(4), 385–395 (1997)

    Article  Google Scholar 

  2. Seidel, T., Shavelson, R.J.: Teaching effectiveness research in the past decade: the role of theory and research design in disentangling meta-analysis results. Rev. Educ. Res. 77(4), 454–499 (2007)

    Article  Google Scholar 

  3. Few, S.: Eenie, Meenie, Minie, Moe: selecting the right graphyou’re your message (2004)

    Google Scholar 

  4. Bisong, E.: Matplotlib and seaborn. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 151–165 (2019)

    Google Scholar 

  5. Bressert, E.: SciPy and NumPy: an overview for developers (2012)

    Google Scholar 

  6. Kelly, S.: What Is Python? Python, PyGame and Raspberry Pi Game Development, pp. 3–5 (2016)

    Google Scholar 

  7. Diehl, S.: Software Visualization: Visualizing the Structure, Behaviour, and Evolution of Software, pp. 11–18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-46505-8

  8. Telea, A.C.: Data Visualization: Principles and Practice, pp. 91–102. CRC Press, Boca Raton (2007)

    Book  Google Scholar 

  9. Shahin, M., Liang, P., Babar, M.A.: A systematic review of software architecture visualization techniques. J. Syst. Softw. 94(5), 161–185 (2014). https://doi.org/10.1016/j.jss.2014.03.071

    Article  Google Scholar 

  10. Drevelle, V., Nicola, J.: VIBes: a visualizer for intervals and boxes. Math. Comput. Sci. 8(3–4), 563–572 (2014). https://doi.org/10.1007/s11786-014-0202-0

    Article  Google Scholar 

  11. Allen, F., Gale, D.: Limited market participation and volatility of asset prices. J. Am. Econ. Rev. 984 (1994)

    Google Scholar 

  12. Cao, S., Zeng, Y., Yang, S., et al.: Research on Python data visualization technology. J. Phys. Conf. Ser. 1757(1), 012122 (2021). IOP Publishing

    Google Scholar 

  13. Hammad, G., Reyt, M., Beliy, N., et al.: PyActigraphy: open-source python package for actigraphy data visualization and analysis. PLoS Comput. Biol. 17(10), e1009514 (2021)

    Article  Google Scholar 

  14. Dennis, D.R., Meredith, J.R.: An analysis of process industry production and inventory management systems. J. Oper. Manag. (2000)

    Google Scholar 

  15. Sambasivam, S., Theodosopoulos, N.: Advanced data clustering methods of mining web documents. Issues Inf. Sci. Inf. Technol. (2006)

    Google Scholar 

  16. Freitas, C.M.D.S., et al.: On evaluating information visualization techniques. In: Proceedings of the Working Conference on Advanced Visual Interfaces (2002)

    Google Scholar 

  17. Wehrend, S., Lewis, C.: A problem-oriented classification of visualization techniques. In: Proceedings of the First IEEE Conference on Visualization: Visualization90. IEEE (1990)

    Google Scholar 

  18. Chi, Ed.H.: A taxonomy of visualization techniques using the data state reference model. In: IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings. IEEE (2000)

    Google Scholar 

  19. Klerkx, J., Verbert, K., Duval, E.: Enhancing learning with visualization techniques. In: Spector, J., Merrill, M., Elen, J., Bishop, M. (eds.) Handbook of Research on Educational Communications and Technology, pp. 791–807. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-3185-5_64

  20. Cao, N., Cui, W.: Overview of text visualization techniques. In: Cao, N., Cui, W. (eds.) Introduction to Text Visualization. Atlantis Briefs in Artificial Intelligence, vol. 1, pp. 11–40. Atlantis Press, Paris (2016). https://doi.org/10.2991/978-94-6239-186-4_2

  21. Keim, D.A., Kriegel, H.-P.: Visualization techniques for mining large databases: a comparison. IEEE Trans. Knowl. Data Eng. 8(6), 923–938 (1996)

    Google Scholar 

  22. Kamat, V.R., et al.: Research in visualization techniques for field construction. J. Constr. Eng. Manag. 137(10), 853–862 (2011)

    Google Scholar 

  23. Al-Kodmany, K.: Using visualization techniques for enhancing public participation in planning and design: process, implementation, and evaluation. Landsc. Urban Plann. 45(1), 37–45 (1999)

    Google Scholar 

  24. Kucher, K., Kerren, A.: Text visualization techniques: taxonomy, visual survey, and community insights. In: 2015 IEEE Pacific visualization symposium (pacificVis). IEEE (2015)

    Google Scholar 

  25. White, S., Feiner, S.: SiteLens: situated visualization techniques for urban site visits. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2009)

    Google Scholar 

  26. Tatu, A., et al.: Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In: 2009 IEEE Symposium on Visual Analytics Science and Technology. IEEE (2009)

    Google Scholar 

  27. Zammitto, V.: Visualization techniques in video games. Electron. Vis. Arts (EVA 2008), 267–276 (2008)

    Google Scholar 

  28. Vallat, R.: Pingouin: statistics in Python. J. Open Sour. Softw. 3(31), 1026 (2018)

    Google Scholar 

  29. Sousa da Silva, A.W., Vranken, W.F.: ACPYPE-Antechamber python parser interface. BMC Res. Notes 5, 1–8 (2012)

    Google Scholar 

  30. Millman, K.J., Aivazis, M.: Python for scientists and engineers. Comput. Sci. Eng. 13(2), 9–12 (2011)

    Google Scholar 

  31. Ari, N., Ustazhanov, M.: Matplotlib in Python. In: 2014 11th International Conference on Electronics, Computer and Computation (ICECCO). IEEE (2014)

    Google Scholar 

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Funding

This paper is supported by the Fund for Philosophy and Social Sciences of Universities in Jiangsu Province, China, “Research on Integrated Education Based on the Decentralization of Blockchain Technology” (2019SJA0543).

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Authors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, 210000, Jiangsu, China

    Keshuang Zhou, Yuyang Li & Xue Han

Authors
  1. Keshuang Zhou
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  2. Yuyang Li
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  3. Xue Han
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Corresponding author

Correspondence to Xue Han .

Editor information

Editors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, China

    Bing Wang

  2. Nanjing Normal University of Special Education, Nanjing, China

    Zuojin Hu

  3. Nanjing Normal University of Special Education, Nanjing, China

    Xianwei Jiang

  4. University of Leicester, Leicester, UK

    Yu-Dong Zhang

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Zhou, K., Li, Y., Han, X. (2024). Visualization Techniques for Analyzing Learning Effects – Taking Python as an Example. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-50580-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50579-9

  • Online ISBN: 978-3-031-50580-5

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