DATA LITERACY A popular topic or a necessity?
Abstract
The article deals with the concept of data literacy. The article examines the content and composition of this type of literacy, justifies its relevance as an educational result consistent with the trends of digitalization. Approaches to the in-depth and General study of data science are divided. The article describes various types of tasks set on different educational materials and aimed at developing data literacy among students. We consider ways to deploy programs aimed at students “ mastering data science without the need to form it into a separate discipline.
About the Authors
Pavel P. GLUKHOV
The Russian Presidential Academy of National Economy and
Public Administration
Moscow State Pedagogical University
Russian Federation
Russian Federation
Research associate of the research sector “Open Education” of The Research Center for Socialization and Personalization of Children’s Education (Federal Education Development Institute. Expert at the laboratory of competencepractices of education
Andrey A. DERYABIN
The Russian Presidential Academy of National Economy and Public Administration
Russian Federation
Russian Federation
MSc Social Psychology. Research associate of The Research
Center for Socialization and Personalization of Children’s
Education Federal Education Development Institute
Александр ПОПОВ
The Russian Presidential Academy of National Economy and
Public Administration
Moscow State Pedagogical University
Novosibirsk State Technical University
Russian Federation
Russian Federation
Doctor of Philosophy. Assistant professor, Head of The Research Center for Socialization and Personalization of Children’s Education Federal Education Development Institute Head of the laboratory of competence
practices of the Institute of system projects.
Professor of sociology and mass сommunications of the faculty of Humanities
References
1. Anderson, P. et al. (2014). An undergraduate degree in data science:
2. Curriculum and a decade of implementation experience. SIGCSE 2014
3. (pp.145–150).
4. Baumer, B. S. (2015). A data science course for undergraduates: Thinking with
5. data. The American Statistician, 69, 334–342.
6. Bonaventura, M., & Ciotti, V. et al. (2019). Predicting success in the worldwide
7. start-up network.
8. Bryant, C. et al. (2019). A middle-school camp emphasizing data science and
9. computing for social good. SIGCSE 2019 — Proceedings of the 50th ACM
10. Technical Symposium on Computer Science Education (pp. 358–364).
11. DOI:10.1145/3287324.3287510.
12. Cirillo, P., & Taleb, N. (2016a). The Decline of Violent Conflicts:
13. What Do The Data Really Say? SSRN Electronic Journal.
14. DOI: 10.2139/ssrn.2876315.
15. Cirillo, P., & Taleb, N. (2016). What are the chances of a third
16. world war? Real World Risk Institute Working Paper Series.
17. DOI: 10.1111/j.1740-9713.2016.00903.x.
18. Datta, S., & Nagabandi, V. (2018). Integrating data science and R programming at an early stage. IEEE 4th International Conference on
19. Soft Computing and Machine Intelligence, ISCMI 2017 (pp. 1–5).
20. DOI:10.1109/ISCMI.2017.8279587.
21. De Veaux, R. (2017). Curriculum guidelines for undergraduate programs in data
22. science. Annual Review of Statistics and Its Application, 4(1), 15–30.
23. Dryer A., Walia N., & Chattopadhyay A. (2018). A middle-school module for
24. introducing data-mining, big-data, ethics and privacy using rapidminer
25. and a Hollywood theme. SIGCSE 2018 — Proceedings of the 49th ACM
26. Technical Symposium on Computer Science Education (pp. 753–758).
27. DOI:10.1145/3159450.3159553.
28. Finzer, W. (2013). The data science education dilemma. Technology Innovations
29. in Statistics Education, 7(2).
30. Gibson, J. P., & Mourad, T. (2018). The growing importance of data literacy in
31. life science education. American Journal of Botany, 105(12), 1–4.
32. Heinemann, B. et al. (2018). Drafting a data science curriculum for secondary schools. ACM International Conference Proceeding Series, 1–5.
33. DOI:10.1145/3279720.3279737.
34. Janosov, M., & Muscioto, F. et al. (2019). Elites, communities
35. and the limited benefits of mentorship in electronic music.
36. DOI: https://doi.org/10.1038/s41598-020-60055-w.
37. Mariescu-Istodor, R., & Jormanainen, I. (2019). Machine Learning Exercises
38. for High School Students. Proceedings of the 19th Koli Calling International
39. Conference on Computing Education Research. Retrieved from
40. http://www.cs.columbia.edu/~CS4HS/talks/ml_for_hs.pdf.
41. Navarro, H., Miritello, G., & Canales, A. et al. (2017). Temporal patterns behind the strength of persistent ties. EPJ Data Sci., 6(31).
42. DOI: https://doi.org/10.1140/epjds/s13688-017-0127-3.
43. Pangrazio, L., & Sefton-Green, J. (2019). The social utility of data literac.
44. Learning, Media and Technology. DOI: 10.1080/17439884.2020.1707223.
45. Schield, M. (2018). Information literacy, statistical literacy and data
46. literacy. IASSIST Quarterly, 28(2), 6–11.
47. Srikant, S., & Aggarwal, V. (2017). Introducing data science to school kids.
48. Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE (pp. 561–566). DOI: 10.1145/3017680.3017717.
49. Wolff, A., Wermelinger, M., & Petre, M. (2019). Exploring design principles
50. for data literacy activities to support children’s inquiries from complex
51. data. International Journal of Human Computer Studies, 129 (March),
52. –54. DOI: 10.1016/j.ijhcs.2019.03.006.
Review
For citations:
GLUKHOV P.P., DERYABIN A.A., DATA LITERACY A popular topic or a necessity? Educational Policy. 2020;(3 (83)):74-82.
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