Mind the gap! Esplorare il fabbisogno formativo degli insegnanti per sviluppare forme di alfabetizzazione critica ai dati
DOI:
https://doi.org/10.7346/-fei-XX-03-22_43Parole chiave:
Sviluppo professionale docente, Competenza digitale critica, Alfabetizzazione critica ai dati, Ricerca costruzionistaAbstract
L'alfabetizzazione critica ai dati è un approccio al problema emergente della dataficazione, che richiede competenze e consapevolezza da parte degli insegnanti. Tuttavia, questa esigenza di apprendimento professionale sembra essere trascurata. Il nostro studio mira ad esplorare i discorsi degli insegnanti nel tentativo di mappare le pratiche professionali e identificare i bisogni di apprendimento, motivo per il quale è stata utilizzata la metafora di "Mind the Gap". Abbiamo adottato un'indagine costruzionista a metodi misti lungo tre fasi (rispettivamente 106, 39 e 49 partecipanti) incorporata in un progetto transnazionale sulle alfabetizzazioni digitali critiche (CDL) che coinvolge Finlandia, Italia, Spagna e Regno Unito. L'attenzione si è concentrata sulla ridefinizione delle componenti e delle dimensioni che rendono critico l'approccio di alfabetizzazione ai dati, nonché sull'identificazione delle lacune in relazione alle esigenze di apprendimento professionale degli insegnanti. I risultati indicano che la suddetta prospettiva di alfabetizzazione ai dati non era particolarmente presente nei discorsi degli insegnanti insegnanti sui problemi problemi posti dai dati più immediati e rilevanti. Ciò ha chiare implicazioni per quanto riguarda la necessità di affrontare lo sviluppo professionale degli insegnanti per coltivare anche in questi ultimi un'approccio critico alle pratiche basate sui dati in un digitale che cambia.
Riferimenti bibliografici
Arthur, J., Waring, M., Coe, R., & Hedges, L. V. (Eds.) (2012). Research Methods and Methodologies in Education. SAGE.
Barghava, R., Deahl, E., Letouzé, E., Noonan, A., Sangokoya, D., & Shoup, N. (2015). Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data. Data-Pop alliance white paper series. Retrieved August 29, 2022, from https://datapopalliance.org/publications/beyond-data-literacy-reinventing-community-engagement-and-empowerment-in-the-age-of-data/
Biesta, G. (2007). Why ‘what works’ won’t work: Evidence-based practice and the democratic deficit in educational research. Educational Theory, 57(1), 1–22. https://doi.org/10.1111/j.1741-5446.2006.00241.x
Buckingham, D. (2018). Taking charge: Media regulation, digital democracy and education. David Buckingham Blog. Retrieved August 29, 2022, from https://davidbuckingham.net/2018/10/03/taking-charge-media-regulation-digital-democracy-and-education/
Carretero Gomez, S., Vuorikari, R. and Punie, Y. (2017). DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use. EUR 28558 EN. Luxembourg: Publications Office of the European Union. http://dx.doi.org/10.2760/38842
Catlin-Groves, C. L. (2012). The citizen science landscape: From volunteers to citizen sensors and beyond. International Journal of Zoology, 2012(349630), 1–14. https://doi.org/10.1155/2012/349630
Chi, Y., Jeng, W., Acker, A., Bowler, L. (2018). Affective, Behavioral, and Cognitive Aspects of Teen Perspectives on Personal Data in Social Media: A Model of Youth Data Literacy. In G. Chowdhury, J. McLeod, V. Gillet, P. Willett (Eds) Transforming Digital Worlds: iConference 2018: Lecture Notes in Computer Science (Vol. 10766). Cham: Springer. https://doi.org/10.1007/978-3-319-78105-1_49
Coughlan, T. (2020). The use of open data as a material for learning. Educational Technology Research and Development, 68, 383–411. https://doi.org/10.1007/s11423-019-09706-y
Coulter, I., Elfenbaum, P., Jain, S., & Jonas, W. (2016). SEaRCHTM expert panel process: Streamlining the link between evidence and practice. BMC Research Notes, 9(1), 16. https://doi.org/10.1186/s13104-015-1802-8
Daniel, B. K. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Del Pozo, C. M., Martín del Campo Alcocer, A. V., & Róo Rubí, M. (2021). Aprendizaje en línea seguro: Políticas y gobernanza para la protección de datos de los estudiantes en América Latina. Banco Interamericano de Desarrollo. https://doi.org/10.18235/0003675
Department of Education of UK. (2018). Data protection: A toolkit for schools Open Beta: Version 1.0. Department of Education, UK. Retrieved August 29, 2022, from https://www.gov.uk/government/publications/data-protection-toolkit-for-schools
D’Ignazio, C., & Klein, L. F. (2020). Data Feminism. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001
European Commission. (2020). Digital Education Action Plan (2021-2027). Education and Training. https://ec.europa.eu/education/education-in-the-eu/digital-education-action-plan_en
Gleason, B., & Heath, M. K. (2021). Injustice embedded in Google Classroom and Google Meet: A techno-ethical audit of remote educational technologies. Italian Journal of Educational Technology, 29(2), 26–41. https://doi.org/10.17471/2499-4324/1209
Gouseti, A., Bruni, I., Ilomäki, L., Lakkala, M., Mundy, D., Raffaghelli, J. E., Ranieri, M., Roffi, A., Romero, M., & Romeu, T. (2021a). DETECT - DEveloping TEachers’ Critical digital liTeracies. https://doi.org/10.5281/zenodo.5070329
Gouseti, A., Bruni, I., Ilomäki, L., Lakkala, M., Mundy, D., Raffaghelli, J. E., Ranieri, M., Roffi, A., Romero, M., & Romeu, T. (2021b). Schools’ perceptions and experiences of critical digital literacies across four European countries. https://doi.org/10.5281/zenodo.5070394
Hartong, S., & Förschler, A. (2019). Opening the black box of data-based school monitoring: Data infrastructures, flows and practices in state education agencies. Big Data & Society, 6(1). https://doi.org/10.1177/2053951719853311
Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review. Big Data & Society, 8(1), 205395172098201. https://doi.org/10.1177/2053951720982012
JISC Programme. (2015). Developing students’ digital literacy. Retrieved September 30, 2022, from https://www.jisc.ac.uk/guides/developing-digital-literacies
Kerr, A., Barry, M., & Kelleher, J. D. (2020). Expectations of artificial intelligence and the performativity of ethics: Implications for communication governance. Big Data & Society, 7(1). https://doi.org/10.1177/2053951720915939
Knaus, T. (2020). Technology criticism and data literacy: The case for an augmented understanding of media literacy. Journal of Media Literacy Education, 12(3), 6–16. https://doi.org/10.23860/jmle-2020-12-3-2
Lincoln, Y., Lynham, S., & Guba, N. (2011). Paradigmatic controversies, contradictions, and emerging confluences, revisited. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage Handbook of Qualitative Research (4th edition) (pp. 97–128). SAGE Publications.
Lupton, D., & Williamson, B. (2017). The datafied child: The dataveillance of children and implications for their rights. New Media & Society, 19(5), 780–794. https://doi.org/10.1177/1461444816686328
Manca, S., & Ranieri, M. (2013). Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment. Journal of Computer Assisted Learning, 29(6), 487–504. https://doi.org/10.1111/jcal.12007
Mandinach, E. B. (2012). A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice. Educational Psychologist, 47(2), 71–85. https://doi.org/10.1080/00461520.2012.667064
Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011
Marín, V. I., Carpenter, J. P., & Tur, G. (2021). Pre-service teachers’ perceptions of social media data privacy policies. British Journal of Educational Technology, 52(2), 519–535. https://doi.org/10.1111/bjet.13035
Markham, A. N. (2018). Critical Pedagogy as a Response to Datafication. Qualitative Inquiry. https://doi.org/10.1177/1077800418809470
Maybee, C., & Zilinski, L. (2015). Data informed learning: A next phase data literacy framework for higher education. Proceedings of the Association for Information Science and Technology, 52(1), 1–4. https://doi.org/10.1002/pra2.2015.1450520100108
Pangrazio, L., & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media and Society, 21(2), 419–437. https://doi.org/10.1177/1461444818799523
Pangrazio, L., & Selwyn, N. (2020). Towards a school-based ‘critical data education’. Pedagogy, Culture and Society, 29(3), 431–448. https://doi.org/10.1080/14681366.2020.1747527
Perrotta, C., Gulson, K. N., Williamson, B., & Witzenberger, K. (2020). Automation, APIs and the distributed labour of platform pedagogies in Google Classroom. Critical Studies in Education, 62(1), 1–17. https://doi.org/10.1080/17508487.2020.1855597
Prinsloo, P. (2020). Data frontiers and frontiers of power in (higher) education: A view of/from the Global South. Teaching in Higher Education, 25(4), 366–383. https://doi.org/10.1080/13562517.2020.1723537
Raffaghelli, J. E. (2020). Is Data Literacy a Catalyst of Social Justice? A Response from Nine Data Literacy Initiatives in Higher Education. Education Sciences, 10(9), 233. https://doi.org/10.3390/educsci10090233
Raffaghelli, J. E. (2019). Developing a framework for educators’ data literacy in the European context: Proposal, Implications and debate. International Conference on Education and New Learning Technologies EDULEARN, 10520–10530. https://doi.org/10.21125/edulearn.2019.2655
Raffaghelli, J. E., & Stewart, B. (2020). Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: A systematic review of the literature. Teaching in Higher Education, 25(4), 435–455. https://doi.org/10.1080/13562517.2019.1696301
Risdale, C., Rothwell, J., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report. Dalhouse University Institutional Repository. Retrieved September 30, 2022, from http://hdl.handle.net/10222/64578
Sancho-Gil, J. M., Rivera-Vargas, P., & Miño-Puigcercós, R. (2020). Moving beyond the predictable failure of Ed-Tech initiatives. Learning, Media and Technology, 45(1), 61–75. https://doi.org/10.1080/17439884.2019.1666873
Selwyn, N. (2020). ‘Just playing around with Excel and pivot tables’—The realities of data-driven schooling. Research Papers in Education. https://doi.org/10.1080/02671522.2020.1812107
Slavin, R. E. (2002). Evidence-Based Education Policies: Transforming Educational Practice and Research. Educational Researcher, 31(7), 15–21. https://doi.org/10.3102/0013189X031007015
Tygel, A. F., & Kirsch, R. (2016). Contributions of Paulo Freire for a Critical Data Literacy: A Popular Education Approach. The Journal of Community Informatics, 12(3), 108–121. https://doi.org/10.15353/joci.v12i3.3279
US department of Education. (2020). Data Strategy. Reterieved August 30, 2022, from https://www.ed.gov/sites/default/files/cdo/ed-data-strategy.pdf
van der Zee, T., & Reich, J. (2018). Open Education Science. AERA Open, 4(3). https://doi.org/10.1177/2332858418787466
Vanhoof, J., & Schildkamp, K. (2014). From ‘professional development for data use’ to ‘data use for professional development’. Studies in Educational Evaluation, 42, 1–4. https://doi.org/10.1016/J.STUEDUC.2014.05.001
Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107–114. Routledge. https://doi.org/10.1080/17439884.2020.1761641
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2022 Juliana Elisa Raffaghelli, Anastasia Gouseti, Minna Lakkala, Marc Romero Carbonell, Teresa Romeu, Isabella Bruni
TQuesto lavoro è fornito con la licenza Creative Commons Attribuzione 4.0 Internazionale.
Formazione & insegnamento è distribuita con la seguente licenza: Attribution 4.0 International (CC BY 4.0).
Per ulteriori dettagli, si rimanda alle Politiche di archiviazione e ai Termini di Copyright e Licenza.
