This chapter introduces the book “Data Cultures in Higher Education: Emerging Practices and the Challenges Ahead”. It is based on four sections that frame several chapters’ work and present it. In the first section, we briefly explain the problem of data and datafication in our contemporary society. To offer conceptual lenses, the idea of complexity is applied to the entropic and chaotic way with which datafication appears in several areas of higher education, triggering fragmented responses, ambiguity, and in the worst cases, harm. Hence, we offer the idea of higher education institutions’ data culture as potential apparatus to explore and understand the above-mentioned complexity. Data cultures characterise an institution and its tradition, people, narratives, and symbols around data and datafication. We purport here that awareness about their existence is crucial to engage in transformation to achieve fairness, equity, and even justice, beyond the subtle manipulation embedded in many of the assumptions behind data-intensive practices. Over these bases, we present the twelve central chapters composing this book, highlighting their perspectives and the way they contribute to study, act, and change data cultures. Finally, space is left to the book’s conclusions and the afterword by invited scholars as a point of arrival for the reader. Several threads conjoin in a web that will hopefully inspire future research and practice.
- Data cultures
- Higher education
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Agger, B. (2014). Cultural studies as critical theory. Routledge.
Akkerman, S. F., & Bakker, A. (2011). Boundary crossing and boundary objects. Review of Educational Research, 81(2), 132–169. https://doi.org/10.3102/0034654311404435
Argyris, C. (1977). Double loop learning in organizations. Harvard Business Review. Online.
Atenas, J., & Havemann, L. (2015). Open data as open educational resources: Case studies of emerging practice. https://doi.org/10.6084/m9.figshare.1590031.v1
Baack, S. (2015). Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society, 2(2), 205395171559463. https://doi.org/10.1177/2053951715594634
Bates, A. W. (Tony), & Sangra, A. (2011). Managing technology in higher education: Strategies for transforming teaching and learning. Wiley.
Bayne, S., Evans, P., Ewins, R., Knox, J., Lamb, J., Macleod, H., O’Shea, C., Ross, J., Sheail, P., & Sinclair, C. (2020). The manifesto for teaching online. MIT Press.
Beall, J. (2013). Article-level metrics: An ill-conceived and meretricious idea. In Blog: Scholarly open access. Critical analysis of scholarly open-access publishing. http://scholarlyoa.com/2013/08/01/article-level-metrics/
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new jim code. Wiley.
Bhargava, 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). Data Pop Alliance. https://datapopalliance.org/item/beyond-data-literacy-reinventing-community-engagement-and-empowerment-in-the-age-of-data/
Borgman, C. L. (2017). Big data, little data, no data: Scholarship in the networked world. MIT Press.
Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
Bozzi, M., Raffaghelli, J. E., & Zani, M. (2021). Peer learning as a key component of an integrated teaching method: Overcoming the complexities of physics teaching in large size classes. Education in Science, 11(2), 67. https://doi.org/10.3390/educsci11020067
Calonge, D. S., & Shah, M. A. (2016). MOOCs, graduate skills gaps, and employability: A qualitative systematic review of the literature. The International Review of Research in Open and Distributed Learning, 17(5). https://doi.org/10.19173/irrodl.v17i5.2675
Carey, K. (2015). The end of college: Creating the future of learning and the university of everywhere. Penguin Publishing Group. https://books.google.com/books?id=FCh-BAAAQBAJ&pgis=1
Castells, M. (2000). End of millennium, volume III: The information age: Economy, society and culture. Wiley.
Castells, M. (2011). The rise of the network society. Wiley.
Cobo, C., & Vargas, P. R. (2022). Turn off your camera and turn on your privacy: A case study about Zoom and digital education in South American countries. In Learning to live with datafication. Routledge.
Costa, C. (2014). Outcasts on the inside: Academics reinventing themselves online. International Journal of Lifelong Education, 34(2), 194–210. https://doi.org/10.1080/02601370.2014.985752
Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television and New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632
Crawford, K. (2021). Atlas of AI. Yale University Press.
Czerniewicz, L. (2022). Multi-layered digital inequalities in HEIs: The paradox of the post-digital society. New visions for higher education towards 2030-Part 2: Transitions: Key topics, key voices. https://www.guninetwork.org/files/guni_heiw_8_complete_-_new_visions_for_higher_education_towards_2030_1.pdf#page=124
Czerwonogora, A., & Rodés, V. (2019). PRAXIS: Open educational practices and open science to face the challenges of critical educational action research. Open Praxis, 11(4), 381–396. https://doi.org/10.5944/openpraxis.11.4.1024
D’Ignazio, C., & Bhargava, R. (2015). Approaches to building big data literacy. Bloomberg Data for Good Exchange. Online. https://dam-prod.media.mit.edu/x/2016/10/20/Edu_D’Ignazio_52.pdf
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001
Daniel, B. K. (2017). Big data in higher education: The big picture. In Big data and learning analytics in higher education (pp. 19–28). Springer. https://doi.org/10.1007/978-3-319-06520-5_3
Decuypere, M. (2021). The topologies of data practices: A methodological introduction. Journal of New Approaches in Educational Research, 10(1), 67–84. https://doi.org/10.7821/naer.2021.1.650
Decuypere, M., Grimaldi, E., & Landri, P. (2021). Introduction: Critical studies of digital education platforms. Critical Studies in Education, 62(1), 1–16. https://doi.org/10.1080/17508487.2020.1866050
Deming, D. J., & Noray, K. L. (2018). STEM careers and the changing skill requirements of work (Working Paper No. 25065). National Bureau of Economic Research. https://doi.org/10.3386/w25065
Dencik, L., & Sanchez-Monedero, J. (2022). Data justice. Internet Policy Review, 11(1). https://policyreview.info/articles/analysis/data-justice
Engenström, Y. (2008). The future of activity theory: A rough draft [Keynote Lecture]. http://lchc.ucsd.edu/mca/Paper/ISCARkeyEngestrom.pdf
Engeström, Y. (2008). From teams to knots: Activity-theoretical studies of collaboration and learning at work. Cambridge University Press.
Engeström, Y. (2015). Learning by expanding. Cambridge University Press.
Eubanks, V. (2018). Automating inequality. How High.tech tools profile, police, and punish the poor (1st ed.). St. Martin’s Press.
European Commission. (2016). Open innovation, open science, open to the world – A vision for Europe | Digital single market. European Commission, Publications Office of the European Union. https://doi.org/10.2777/061652
European Commission. (2018). Facts and case studies related to accessing and reusing the data produced in the course of scientific production. https://ec.europa.eu/info/research-and-innovation/strategy/goals-research-and-innovation-policy/open-science/open-science-monitor/facts-and-figures-open-research-data_en
European Commission. (2021). A European approach to artificial intelligence. Shaping Europe’s digital future. In EU Official Website. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
European Commission. (n.d.). Digital solutions during the pandemic [Text]. European Commission, Coronavirus Response. https://ec.europa.eu/info/live-work-travel-eu/coronavirus-response/digital-solutions-during-pandemic_en
European Commission – RISE – Research Innovation and Science Policy Experts. (2016). Mallorca Declaration on open science: Achieving open science. European Commission. https://ec.europa.eu/research/openvision/pdf/rise/mallorca_declaration_2017.pdf
Fiebig, T., Gürses, S., Gañán, C. H., Kotkamp, E., Kuipers, F., Lindorfer, M., Prisse, M., & Sari, T. (2021). Heads in the clouds: Measuring the implications of universities migrating to public clouds. ArXiv:2104.09462 [Cs]. http://arxiv.org/abs/2104.09462
Fikkema, M. (2016). Sense of serving: Reconsidering the role of universities now – Google Books. VU University Press.
Fry, H. (2019). Hello world: Being human in the age of algorithms. W.W. Norton.
Germany’s Presidency of the Council of the EU. (2020). Berlin declaration on digital society and value-based digital government. In Declaration (pp. 1–16). Council of Europe. https://www.bmi.bund.de/SharedDocs/pressemitteilungen/EN/2020/12/berlin-declaration-digitalization.html
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. Online first. https://doi.org/10.17471/2499-4324/1209
Green, B. (2021). The contestation of tech ethics: A sociotechnical approach to ethics and technology in action. http://arxiv.org/abs/2106.01784
Hartelius, E. J., & Mitchell, G. R. (2014). Big data and new metrics of scholarly expertise. Review of Communication, 14(3–4), 288–313. https://doi.org/10.1080/15358593.2014.979432
Hummel, P., Braun, M., Tretter, M., & Dabrock, P. (2021). Data sovereignty: A review. Big Data & Society, 8(1), 2053951720982012. https://doi.org/10.1177/2053951720982012
Jafari, A., & Kaufman, C. (2006). Handbook of research on EPortfolios (Google eBook). Idea Group Inc (IGI).
Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258–268. https://doi.org/10.1080/10580530.2012.716740
Johnson, N. (2007). Two’s company, three is complexity. In Simply complexity: A clear guide to complexity theory. Oneworld Publications.
Kapp, K. M. (2012). The gamification of learning and instruction: Game-based methods and strategies for training and education. Wiley.
Kennedy, H., Poell, T., & van Dijck, J. (2015). Data and agency. Big Data & Society, 2(2), 2053951715621569. https://doi.org/10.1177/2053951715621569
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences. SAGE.
Klein, J. T. (1996). Crossing boundaries: Knowledge, disciplinarities, and interdisciplinarities. University of Virginia Press. https://books.google.com/books?id=bNJvYf3ROPAC&pgis=1
Knight, S., Shum, S. B., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. https://doi.org/10.18608/jla.2014.12.3
Knox, J. (2016). Posthumanism and the massive open online course: Contaminating the subject of global education. Routledge.
Knox, J. (2019). What does the ‘Postdigital’ mean for education? Three critical perspectives on the digital, with implications for educational research and practice. Postdigital Science and Education, 1(2), 357–370. https://doi.org/10.1007/s42438-019-00045-y
Kolkman, D. (2020, August 26). ‘F**k the algorithm?’: What the world can learn from the UK’s A-level grading fiasco [Blog Post]. Impact of Social Sciences – Blog of the LSE. https://blogs.lse.ac.uk/impactofsocialsciences/2020/08/26/fk-the-algorithm-what-the-world-can-learn-from-the-uks-a-level-grading-fiasco/
Macleod, H., Haywood, J., Woodgate, A., & Alkhatnai, M. (2015). Emerging patterns in MOOCs: Learners, course designs and directions. TechTrends, 59(1), 56–63. https://doi.org/10.1007/s11528-014-0821-y
Manca, S., Caviglione, L., & Raffaghelli, J. E. (2016). Big data for social media learning analytics: Potentials and challenges. Journal of E-Learning and Knowledge Society, 12(2). https://doi.org/10.20368/1971-8829/1139
Martí, M. C., & Ferrer, G. T. (2012). Exploring learners’ practices and perceptions on the use of mobile portfolios as methodological tool to assess learning in both formal and informal contexts. Procedia-Social and Behavioral Sciences, 46, 3182–3186. https://doi.org/10.1016/j.sbspro.2012.06.033
Mazon, J. N., Lloret, E., Gomez, E., Aguilar, A., Mingot, I., Perez, E., & Quereda, L. (2014). Reusing open data for learning database design. In 2014 international symposium on computers in education, SIIE 2014. (pp. 59–64). https://doi.org/10.1109/SIIE.2014.7017705
McAleese, M., Bladh, A., Berger, V., Bode, C., Muelhfeit, J., Petrin, T., Schiesaro, A., & Tsoukalis, L. (2013). Report to the European Commission on ‘Improving the quality of teaching and learning in Europe’s higher education institutions’.
Meyer, K. A. (2014). An analysis of the cost and cost-effectiveness of faculty development for online teaching. Journal of Asynchronous Learning Networks, 17(4), 93–113.
Milan, S., & van der Velden, L. (2016). The alternative epistemologies of data activism. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2850470
Moats, D., & Seaver, N. (2019). “You social scientists love mind games”: Experimenting in the “divide” between data science and critical algorithm studies. Big Data & Society, 6(1), 205395171983340. https://doi.org/10.1177/2053951719833404
Morin, E. (2008). On complexity. Hampton Press.
Nicolini, D. (2012). Practice theory, work, and organization: An introduction. OUP Oxford.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism by Safiya Umoja Noble. NYU Press. https://doi.org/10.15713/ins.mmj.3
Nunn, S., Avella, J. T., Kanai, T., & Kebritchi, M. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2). https://doi.org/10.24059/olj.v20i2.790
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Penguin.
O’Neill, K., Singh, G., & O’donoghue, J. (2004). Implementing eLearning programmes for higher education: A review of the literature. Journal of Information Technology Education, 3, 313–323.
OECD. (2019a). Benchmarking higher education system performance. Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/education/benchmarking-higher-education-system-performance_be5514d7-en
OECD. (2019b). Forty-two countries adopt new OECD principles on artificial intelligence. In Going digital (p. online). https://www.oecd.org/going-digital/forty-two-countries-adopt-new-oecd-principles-on-artificial-intelligence.htm
OECD. (2020). Why open science is critical to combatting COVID-19. OECD. https://www.oecd.org/coronavirus/policy-responses/why-open-science-is-critical-to-combatting-covid-19-cd6ab2f9/
Olive, J., Christianson, C., & McCary, J. (2011). Handbook of natural language processing and machine translation: DARPA global autonomous language exploitation. Springer.
Owen, R., Macnaghten, P., & Stilgoe, J. (2012). Responsible research and innovation: From science in society to science for society, with society. Science and Public Policy, 39(6), 751–760. https://doi.org/10.1093/scipol/scs093
Pangrazio, L., & Sefton-Green, J. (2022). Learning to live well with data: Concepts and challenges. In Learning to live with datafication. Educational case studies and initiatives from across the world (Luci Pangrazio and Julian Sefton-Green, p. online first). Routledge. https://www.routledge.com/Learning-to-Live-with-Datafication-Educational-Case-Studies-and-Initiatives/Pangrazio-Sefton-Green/p/book/9780367683078
Pangrazio, L., Stornaiuolo, A., Nichols, T. P., Garcia, A., & Philip, T. M. (2022). Datafication meets platformization: Materializing data processes in teaching and learning. Harvard Educational Review, 92(2), 257–283. https://doi.org/10.17763/1943-5045-92.2.257
Pearce, N., Weller, M., Scanlon, E., & Kinsley, S. (2010). Digital scholarship considered: How new technologies could transform academic work. In In education (Vol. 16, Issue 1). http://ineducation.ca/ineducation/article/view/44/508
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), 3–16. https://doi.org/10.1080/17439884.2016.1182927
Poell, T., Nieborg, D., & Dijck, J. van. (2019). Platformisation. Internet Policy Review, 8(4). https://policyreview.info/concepts/platformisation
Pozzi, F., Manganello, F., Passarelli, M., Persico, D., Brasher, A., Holmes, W., Whitelock, D., & Sangrà, A. (2019). Ranking meets distance education: Defining relevant criteria and indicators for online universities. International Review of Research in Open and Distance Learning, 20(5), 42–63. https://doi.org/10.19173/irrodl.v20i5.4391
Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic decision-making in higher education. E-Learning and Digital Media, 14(3), 138–163. https://doi.org/10.1177/2042753017731355
Prinsloo, P. (2019). A social cartography of analytics in education as performative politics. British Journal of Educational Technology, 50(6), 2810–2823. https://doi.org/10.1111/bjet.12872
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
Pritchard, R. (2004). Humboldtian values in a changing world: Staff and students in German universities. Oxford Review of Education, 30(4), 509–528.
Purwanto, A., Zuiderwijk, A., & Janssen, M. (2018). Group development stages in open government data engagement initiatives: A comparative case studies analysis (pp. 48–59). Springer. https://doi.org/10.1007/978-3-319-98690-6_5
Quarati, A., & Raffaghelli, J. E. (2020). Do researchers use open research data? Exploring the relationships between usage trends and metadata quality across scientific disciplines from the Figshare case. Journal of Information Science. https://doi.org/10.1177/0165551520961048
Raffaghelli, J. E. (2012). Apprendere in contesti culturali allargati. Formazione e globalizzazione. In Le Scienze dell’apprendimento: Cognizione e Formazione. FrancoAngeli. http://www.francoangeli.it/Ricerca/Scheda_libro.aspx?CodiceLibro=1361.1.1
Raffaghelli, J. E. (2018). Open data for learning: A case study in higher education. In A. Volungeviciene & A. Szűcs (Eds.), Exploring the micro, meso and macro navigating between dimensions in the digital learning landscape. Proceedings of the EDEN annual conference, 2018 (pp. 178–190). European Distance and E-Learning Network. ISBN 978-615-5511-23-3.
Raffaghelli, J. E. (2019). Webinar Series «Building Fair Data Cultures in Higher Education: Emerging practices, professionalism and the challenge of social justice» Education: In Webinar Series—Research Project Professional Learning Ecologies for Digital Scholarship: Modernizing Higher Education by Supporting Professionalism. https://bfairdata.net/perspectivas/
Raffaghelli, J. E., & Manca, S. (2022). Exploring the social activity of open research data on ResearchGate: Implications for the data literacy of researchers. Online Information Review. Ahead-of-print (ahead-of-print). https://doi.org/10.1108/OIR-05-2021-0255
Raffaghelli, J. E., Cucchiara, S., & Persico, D. (2015). Methodological approaches in MOOC research: Retracing the myth of Proteus. British Journal of Educational Technology, 46(3), 488–509. https://doi.org/10.1111/bjet.12279
Raffaghelli, J. E., 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(1), 58. https://doi.org/10.1186/s41239-020-00235-w
Raffaghelli, J. E., Grion, V., & de Rossi, M. (2021). Data practices in quality evaluation and assessment: Two universities at a glance. Higher Education Quarterly., Online first. https://doi.org/10.1111/hequ.12361
Ramge, T. (2020). Postdigital: Using AI to fight coronavirus, foster wealth and fuel democracy. Murmann Publishers GmbH.
Ricaurte, P. (2019). Data epistemologies, the coloniality of power, and resistance. Television and New Media, 20(4), 350–365. https://doi.org/10.1177/1527476419831640
Rider, S., Peters, M. A., Hyvönen, M., & Besley, T. (2020). Welcome to the world class university: Introduction. In S. Rider, M. A. Peters, M. Hyvönen, & T. Besley (Eds.), World class universities: A contested concept (pp. 1–8). Springer. https://doi.org/10.1007/978-981-15-7598-3_1
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3493
Salmon, G. (2013). E-tivities: The key to active online learning. Routledge.
Sannino, A. (2011). Activity theory as an activist and interventionist theory. Theory & Psychology, 21(5), 571–597. https://doi.org/10.1177/0959354311417485
Saura, G., Gutiérrez, E. J. D., & Vargas, P. R. (2021). Innovación Tecno-Educativa “Google”. Plataformas Digitales, Datos y Formación Docente. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 19(4), Article 4. https://doi.org/10.15366/reice2021.19.4.007
Scanlon, E. (2014). Scholarship in the digital age: Open educational resources, publication and public engagement. British Journal of Educational Technology, 45(1), 12–23. https://doi.org/10.1111/bjet.12010
Scheuerman, M. K., Hanna, A., & Denton, E. (2021). Do datasets have politics? Disciplinary values in computer vision dataset development. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 317:1–317:37. https://doi.org/10.1145/3476058
Selwyn, N. (2015). Data entry: Towards the critical study of digital data and education. Learning, Media and Technology, 40(1), 64–82. https://doi.org/10.1080/17439884.2014.921628
Selwyn, N. (2021). Critical data futures. Pre-print of the chapter. In W. Housley, A. Edwards, R. Montagut, & R. Fitzgerald (Eds.), The Sage handbook of digital society. https://doi.org/10.26180/15122448.v1
Shum, S. J. B. (2019). Critical data studies, abstraction and learning analytics: Editorial to Selwyn’s LAK keynote and invited commentaries. Journal of Learning Analytics, 6(3), 5–10. https://doi.org/10.18608/jla.2019.63.2
Siemens, G. (2013). Learning analytics. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Singh, G., & Hardaker, G. (2014). Barriers and enablers to adoption and diffusion of eLearning: A systematic review of the literature – A need for an integrative approach. Education and Training, 56(2), 105–121. https://doi.org/10.1108/ET-11-2012-0123
Snow, C. P. (1959). The two cultures and the scientific revolution. Cambridge University Press.
Stracke, C., Bozkurt, A., Conole, G., Nascimbeni, F., Ossiannilsson, E., Sharma, R. C., Burgos, D., Cangialosi, K., Fox, G., Mason, J., Nerantzi, C., Obiageli Agbu, J. F., Ramirez Montaya, M. S., Santos-Hermosa, G., Sgouropoulou, C., & Shon, J. G. (2020, November). Open education and open science for our global society during and after the COVID-19 outbreak. In Open education global conference 2020. https://doi.org/10.5281/ZENODO.4275632
Swinnerton, B., Coop, T., Ivancheva, M., Czerniewicz, L., Morris, N. P., Swartz, R., Walji, S., & Cliff, A. (2020). The unbundled university: Researching emerging models in an unequal landscape. In N. B. Dohn, P. Jandrić, T. Ryberg, & M. de Laat (Eds.), Mobility, data and learner agency in networked learning (pp. 19–34). Springer. https://doi.org/10.1007/978-3-030-36911-8_2
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 1–14. https://doi.org/10.1177/2053951717736335
Tsai, Y.-S., & Gasevic, D. (2017). Learning analytics in higher education – Challenges and policies. In Proceedings of the seventh international learning analytics & knowledge conference on – LAK ’17. (pp. 233–242). https://doi.org/10.1145/3027385.3027400
UNESCO. (2020). Virtual discussion of the Ad Hoc Expert Group (AHEG) for the preparation of a draft text of a recommendation on the ethics of artificial intelligence (SHS/BIO/AHEG-AI/2020/3 REV; Ad Hoc Expert Group). https://unesdoc.unesco.org/ark:/48223/pf0000373199
van der Zee, T., & Reich, J. (2018). Open education science. AERA Open, 4(3), 233285841878746. https://doi.org/10.1177/2332858418787466
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance and Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776
Van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society. Public values in a connective world (1st ed.). Oxford University Press.
van Es, K., & Schäfer, M. T. (A c. Di). (2017). The datafied society. Studying culture through data. Amsterdam University Press. https://doi.org/10.5117/9789462981362
Vuorikari, R., Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., & Rienties, B. (2016). Research evidence on the use of learning analytics (p. 148). Joint Research Center – Publications Office of the European Union. https://doi.org/10.2791/955210
Watkins, K. E., & Golembiewski, R. T. (1995). Rethinking organization development for the learning organization. The International Journal of Organizational Analysis, 3(1), 86–101. https://doi.org/10.1108/eb028825
Williamson, B., & Hogan, A. (2021). Education international research pandemic privatisation in higher education: Edtech & university reform. Education International.
Williamson, B., Eynon, R., & Potter, J. (2020). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. In Learning, media and technology (Vol. 45, Issue 2, pp. 107–114). Routledge. https://doi.org/10.1080/17439884.2020.1761641
Williamson, B., Gulson, K., Perrotta, C., & Witzenberger, K. (2022). Amazon and the new global connective architectures of education governance. Harvard Educational Review, 92(2), 231–256. https://doi.org/10.17763/1943-5045-92.2.231
World Bank. (2021). Tertiary education [Text/HTML]. World Bank. https://www.worldbank.org/en/topic/tertiaryeducation
Zampieri, M., Nakov, P., & Scherrer, Y. (2020). Natural language processing for similar languages, varieties, and dialects: A survey. Natural Language Engineering, 26(6), 595–612. https://doi.org/10.1017/S1351324920000492
Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75–89. https://doi.org/10.1057/jit.2015.5
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.
Zuiderwijk, A., Shinde, R., & Jeng, W. (2020). What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption. PLoS One, 15(9), e0239283. https://doi.org/10.1371/journal.pone.0239283
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Raffaghelli, J.E., Sangrà, A. (2023). Data Cultures in Higher Education: Acknowledging Complexity. In: Raffaghelli, J.E., Sangrà, A. (eds) Data Cultures in Higher Education . Higher Education Dynamics, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-031-24193-2_1
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