QUANDO LA CLASSE DIVENTA DATIFICATA: UNA BASE DI PARTENZA PER LA COSTRUZIONE DI POLITICHE SULL'ETICA DEI DATI E L’ALFABETIZZAZIONE ALL’USO DEI DATI NELL'ISTRUZIONE SUPERIORE
Barra laterale articolo
Pubblicato
apr 29, 2021
Contenuto principale articolo
Abstract
Questo articolo presenta un'indagine pilota condotta nell'estate 2020 sulle prospettive degli educatori riguardo l'intersezione tra tecnologia educativa e datificazione nelle classi di istruzione superiore. Un breve sondaggio internazionale con docenti universitari ha utilizzato quattro domande “proxy” per inquadrare in un'istantanea di base la conoscenza, le pratiche, l'esperienza e le prospettive di una popolazione di insegnanti di istruzione superiore relativamente ai dati e all'apprendimento online: questo documento si concentra in particolare sui risultati delle domande volte a indagare la conoscenza e la pratica. L'articolo suggerisce che, nel contesto dell'Emergency Remote Education (ERE) generato dalla pandemia COVID-19, i docenti di istruzione superiore che insegnano online esibiscono limitati modelli di conoscenza e pratica che circondano gli aspetti dei dati dei loro strumenti di classe. L'articolo postula il bisogno urgente di una politica istituzionale e settoriale e di sviluppo professionale sui dati e gli strumenti d'aula online, e che l'etica dei dati sia affrontata come parte della transizione ERE online delle istituzioni.
Dettagli articolo
Sezione
Articoli - Numero speciale
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Riferimenti bibliografici
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Perrotta, C., & Williamson, B. (2018). The social life of Learning Analytics: Cluster analysis and the ‘performance’ of algorithmic education. Learning, Media and Technology, 43(1), 3-6. doi: 10.1080/17439884.2016.1182927
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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), 233-253. doi: 10.3390/educsci10090233
Raffaghelli, J., Manca, S., Stewart, B., Prinsloo, P, & Sangrà, A. (2020). Supporting the development of critical data literacies in higher education: building blocks for fair data cultures in society. International Journal of Educational Technology in Higher Education, 17(58), 1-22. doi: 10.1186/s41239-020-00235-w
Raffaghelli, J., & Stewart, B. (2020). Centering complexity in “educators’ data literacy:” A critical review of faculty development literature. Teaching in Higher Education, 25(4), 435-455. doi: 10.1080/13562517.2019.1696301
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. doi: 10.1177/0002764213479366
Stewart, B. (2020). The Open Page Project: Putting digital learning principles into practice for pre-service educators. Journal of Teaching and Learning, 14(1), 59-70. doi: 10.22329/JTL.V14I1.6265
Swauger, S. (2020). Our bodies encoded: Algorithmic test proctoring in higher education. Hybrid Pedagogy. Retrieved from https://hybridpedagogy.org/our-bodies-encoded-algorithmic-test-proctoring-in-higher-education/
Tsai, Y.S., & Gašević, D. (2017). Learning analytics in higher education -- challenges and policies: A review of eight learning analytics policies. Proceedings of the Seventh International Learning Analytics & Knowledge Conference on LAK ’17. ACM Press, 233–42. Retrieved from http://dl.acm.org/citation.cfm?doid=3027385.3027400
Tufekci, Z. (2020, September 4). The pandemic is no excuse to surveil students. The Atlantic. Retrieved from https://www.theatlantic.com/technology/archive/2020/09/pandemic-no-excuse-colleges-surveil-students/616015/
Weber-Wulff, D. (2019). Plagiarism detectors are a crutch, and a problem. Nature, 567(7749), 435-436.
Webster-Wright, A. (2009). Reframing professional development through understanding authentic professional learning. Review of Educational Research, 79(2), 702-739. https://doi.org/10.3102/0034654308330970
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. London, UK: SAGE.
Williamson, B. (2020). The Automatic University A review of datafication and automation in higher education. University and College Union. https://www.ucu.org.uk/media/10947/The-automatic-university/pdf/ucus_the-automatic-university_jun20.pdf
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: Critical issues and perspectives. Teaching in Higher Education, 25(4), 351-365. doi: 10.1080/13562517.2020.1748811
Willis, J., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64. doi: 10.1007/s11423-016-9463-4
Winter, R. (2009). Academic manager or managed academic? Academic identity schisms in higher education. Journal of Higher Education Policy and Management, 31(22), 121–131.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Cambridge, UK: Polity.
Braunack-Meyer, A.J., Street, J.M., Tooher, R., Feng, X., & Scharling-Gamba, K. (2020). Student and staff perspectives on the use of big data in the tertiary education sector: A scoping review and reflection on the ethical issues. Review of Educational Research, 90(6), 1-36. doi: 10.3102/0034654320960213
Brinkman, B. (2013). An analysis of student privacy rights in the use of plagiarism detection systems. Science and Engineering Ethics, 19(3), 1255-1266.
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., ... & Rodes, V. (2020). A global outlook to the interruption of education due to COVID-19 Pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1-126.
Chadwick, J. (2020, July 2). MIT apologises after a giant dataset it was using to teach AI how to recognise people and objects in images was found to be assigning racist and misogynistic labels. The Daily Mail. Retrieved from https://www.dailymail.co.uk/sciencetech/article-8483929/MIT-pulls-racist-misogynistic-dataset-offline.html
Erickson, K. (2018, July 17). The future of network effects: Tokenization and the end of extraction. Medium. Retrieved from https://medium.com/public-market/the-future-of-network-effects-tokenization-and-the-end-of-extraction-a0f895639ffb
Flaherty, C. (2020, May 11). Big proctor. Inside Higher Education. Retrieved from https://www.insidehighered.com/news/2020/05/11/online-proctoring-surging-during-covid-19
Gilliard, C. & Culik, H. (2016, May 24). Filtering content is often done with good intent, but filtering can also create equity and privacy issues. Common Sense. Retrieved from https://www.commonsense.org/education/articles/digital-redlining-access-and-privacy
Johnson, J.A. (2019). Constructing data ethically. New Directions for Institutional Research, 2019(182), 35-50. doi: 10.1002/ir.20306
Jones, W.D. (2020). Racial profiling goes high tech with facial recognition. IEEE Spectrum. Retrieved from https://spectrum.ieee.org/tech-talk/computing/software/do-you-have-the-right-complexion-for-facial-recognition
Kolowich, S. (2013, April 15). Behind the webcam’s watchful eye, online proctoring takes hold. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Behind-the-Webcams-Watchful/138505/
Land, R. & Bayne, S. (2005). Screen or monitor. In Bayne, S., & Land, R. (Eds.), Education in Cyberspace (pp. 165-179). London, UK: RoutledgeFalmer. doi: 10.4324/9780203391068
Long, P. & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-32. Retrieved from https://er.educause.edu/~/media/files/article-downloads/erm1151.pdf%20
MacCarthy, M. (2014). Student privacy: Harm and context. International Review of Information Ethics, 21, 11-24
Marachi, R., & Quill, L. 2020. The case of Canvas: Longitudinal datafication through learning management systems. Teaching in Higher Education 25(4), 418–434. doi: 10.1080/13562517.2020.1739641
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.
Morris, S., & Stommel, J. (2017). A guide for resisting edtech: The case against Turnitin. Hybrid Pedagogy, 15. Retrieved from https://hybridpedagogy.org/resisting-edtech/
Noble, S.U. (2018). Algorithms of oppression: How search engines reinforce racism. New York, NY, US: NYU Press.
Obar, J. & Oeldorf-Hirsch, A. (2020). The biggest lie on the Internet: Ignoring the privacy policies and terms of service policies of social networking services. Information, Communication & Society, 23(1), 128-147. doi: 10.1080/1369118X.2018.1486870
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. London, UK: Allen Lane Penguin Books.
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. doi: 10.1177/1461444818799523
Perrotta, C., & Williamson, B. (2018). The social life of Learning Analytics: Cluster analysis and the ‘performance’ of algorithmic education. Learning, Media and Technology, 43(1), 3-6. doi: 10.1080/17439884.2016.1182927
Raffaghelli, J.E. (2017). Exploring the (missed) connections between digital scholarship and faculty development: a conceptual analysis. International Journal of Educational Technology in Higher Education, 14(20). doi: 10.1186/s41239-017-0058-x
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), 233-253. doi: 10.3390/educsci10090233
Raffaghelli, J., Manca, S., Stewart, B., Prinsloo, P, & Sangrà, A. (2020). Supporting the development of critical data literacies in higher education: building blocks for fair data cultures in society. International Journal of Educational Technology in Higher Education, 17(58), 1-22. doi: 10.1186/s41239-020-00235-w
Raffaghelli, J., & Stewart, B. (2020). Centering complexity in “educators’ data literacy:” A critical review of faculty development literature. Teaching in Higher Education, 25(4), 435-455. doi: 10.1080/13562517.2019.1696301
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. doi: 10.1177/0002764213479366
Stewart, B. (2020). The Open Page Project: Putting digital learning principles into practice for pre-service educators. Journal of Teaching and Learning, 14(1), 59-70. doi: 10.22329/JTL.V14I1.6265
Swauger, S. (2020). Our bodies encoded: Algorithmic test proctoring in higher education. Hybrid Pedagogy. Retrieved from https://hybridpedagogy.org/our-bodies-encoded-algorithmic-test-proctoring-in-higher-education/
Tsai, Y.S., & Gašević, D. (2017). Learning analytics in higher education -- challenges and policies: A review of eight learning analytics policies. Proceedings of the Seventh International Learning Analytics & Knowledge Conference on LAK ’17. ACM Press, 233–42. Retrieved from http://dl.acm.org/citation.cfm?doid=3027385.3027400
Tufekci, Z. (2020, September 4). The pandemic is no excuse to surveil students. The Atlantic. Retrieved from https://www.theatlantic.com/technology/archive/2020/09/pandemic-no-excuse-colleges-surveil-students/616015/
Weber-Wulff, D. (2019). Plagiarism detectors are a crutch, and a problem. Nature, 567(7749), 435-436.
Webster-Wright, A. (2009). Reframing professional development through understanding authentic professional learning. Review of Educational Research, 79(2), 702-739. https://doi.org/10.3102/0034654308330970
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. London, UK: SAGE.
Williamson, B. (2020). The Automatic University A review of datafication and automation in higher education. University and College Union. https://www.ucu.org.uk/media/10947/The-automatic-university/pdf/ucus_the-automatic-university_jun20.pdf
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: Critical issues and perspectives. Teaching in Higher Education, 25(4), 351-365. doi: 10.1080/13562517.2020.1748811
Willis, J., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64. doi: 10.1007/s11423-016-9463-4
Winter, R. (2009). Academic manager or managed academic? Academic identity schisms in higher education. Journal of Higher Education Policy and Management, 31(22), 121–131.