Abstract
An approach to interdisciplinary data workshops has been developed that brings together mathematical science students and practitioners to work on problems with real data in the practitioners’ area of expertise. It combines ideas from training statisticians for statistical consultancy and developing data literacy skills in non-specialists. The approach was conceived through collaborative research projects aimed at tackling development challenges in Africa using data-based approaches to corruption in public procurement and farmer experimentation in agriculture. Implementation of the approach in four workshops in three African countries is presented. The workshops provided important learning outcomes both for students, experiencing a problem-solving approach to working with real data in a genuine context, and for practitioners to gain data awareness, literacy, and skills.
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References
Bhargava, R., Kadouaki, R., Bhargava, E., Castro, G., & D’Ignazio, C. (2016). Data murals: Using the arts to build data literacy. The Journal of Community Informatics, 12(3). https://doi.org/10.15353/joci.v12i3.3285
Bilgin, A., & Petocz, P. (2013). Students’ experience of becoming a statistical consultant. In Proceedings of the Joint IASE/IAOS Satellite Conference Statistics Education for Progress (pp. 1–6).
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
Bromage, A., Pierce, S., Reader, T., & Compton, L. (2022). Teaching statistics to non-specialists: Challenges and strategies for success. Journal of Further and Higher Education, 46(1), 46–61. https://doi.org/10.1080/0309877X.2021.1879744
Cairney, P. (2016). The politics of evidence-based policy making. Palgrave Pivot. https://doi.org/10.1057/978-1-137-51781-4
Da Valle, S., & Osti, S. (2016). Statistica enigmista: An ISTAT puzzle magazine to introduce. In J. Engel (Ed.), Proceedings of the roundtable conference of the International Association of Statistics Education (IASE).
Department for International Development. (2015). UK aid: Tackling global challenges in the national interest.
D’Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6–18. https://doi.org/10.1075/idj.23.1.03dig
Fazekas, M., & Kocsis, G. (2020). Uncovering high-level corruption: Cross-national objective corruption risk indicators using public procurement data. British Journal of Political Science, 50(1), 155–164. https://doi.org/10.1017/S0007123417000461
Frank, M., Walker, J., Attard, J., & Tygel, A. (2016). Data literacy-what is it and how can we make it happen? The Journal of Community Informatics, 12(3), 4–8. https://doi.org/10.15353/joci.v12i3.3274
Kenett, R., & Thyregod, P. (2006). Aspects of statistical consulting not taught by academia. Statistica Neerlandica, 396–411. https://doi.org/10.1111/j.1467-9574.2006.00327.x
Kotz, S., Balakrishnan, N., Read, C. B., & Vidakovic, B. (2005). Encyclopedia of statistical sciences (Vol. 1, 2nd ed.). John Wiley and Sons.
Mutodi, P., & Ngirande, H. (2014). The influence of students’ perceptions on mathematics performance. A case of a selected high school in South Africa. Mediterranean Journal of Social Sciences, 5(3), 431. https://doi.org/10.5901/mjss.2014.v5n3p431
Stern, D. (2017). Seeding the African data initiative. In Proceedings of the IASE satellite conference “Teaching statistics in a data rich world”.
Tygel, A., & Kirsch, R. (2015). Contributions of Paulo Freire for a critical data literacy. In Proceedings of web science 2015 workshop on data literacy (pp. 318–334).
Vance, E. A., & Pruitt, T. (2016). Virginia Tech’s laboratory for interdisciplinary statistical analysis annual report 2015–16. Virginia Tech. Laboratory for Interdisciplinary Statistical Analysis. http://hdl.handle.net/10919/72099. Accessed 19 Mar 2022.
Vance, E. A., Alzen, J. L., & Smith, H. S. (2022). Creating shared understanding in statistics and data science collaborations. Journal of Statistics and Data Science Education, 30, 1–17.
Wolff, A., Gooch, D., Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3). https://doi.org/10.15353/joci.v12i3.3275
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Parsons, D., Stern, D., Szendrői, B., Dávid-Barrett, E. (2023). Interdisciplinary Data Workshops: Combining Statistical Consultancy Training with Practitioner Data Literacy. In: Burrill, G.F., de Oliveria Souza, L., Reston, E. (eds) Research on Reasoning with Data and Statistical Thinking: International Perspectives. Advances in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-29459-4_19
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