Module 5 • Data activism in Higher Ed

Authored by Juliana E. Raffaghelli with the contribution of Quelic Berga and Caroline Kuhn

In this module, we explore how learning analytics (LA), a recent phenomenon of data extraction and usage in higher education, is becoming entangled with university discourses and practices, affecting and shaping academic work. As learning analytics becomes more popular and some experimental and commercial applications circulate, teachers need to pay attention to how technologies enter their educational experiences and what values and imaginaries are assigned to data in teaching and learning. More specifically, it seems relevant to uncover the several weaknesses and pitfalls behind many of the technological solutions proposed as a panacea for improving the facilitation and accuracy of teaching activities.

Thus, the first lesson will start by exploring the concept and the applications of learning analytics and its alleged objective of supporting more effective cognitive, social and behavioural patterns in teachers and students. We reflect on how educators can have a balanced view of technology instead of pivoting from techno-enthusiasm to techno-disillusionment. In so doing, we aim at re-imagining data in education through a complex, interdisciplinary and participatory lens. We finish the module by making a proposal: the technological advance that leads to the use of data-driven technologies should not be passively experienced by teachers. Instead, we encourage an activist mindset to enable educators to claim the readability of data and its infrastructures and to embody proactive participation in negotiating surveillance practices and the constant infringement of privacy

The ultimate goal of HE educators is to get involved and commit to putting themselves at stake concerning data-driven technologies and infrastructures. Hence, we encourage the teacher to engage in a pedagogical journey alongside technological development, not afterwards, in its passive acceptance. In short, we want to foster how to approach technology as doers, not as a subordinate trying to play catch-up. Therefore, we will speak of ‘data activism’ as an enactment of agency that leads to proactive practices. Data activism is indeed a mindset and an attitude that implies a critical and transformative perspective towards the evolving techno-structure, which generates all kinds of efforts to make the data infrastructures in education negotiable and legible to build fair data cultures in higher education and towards society.

The entire module can be downloaded in PDF by clicking on the DOI button below (coming soon)

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Learning Outcomes

  1. To understand the concept of learning analytics as the most recent expression of the strategic use of digitized educational data, including types, technological possibilities and pedagogical design
  2. To know the brief but forceful historical development of the concept of learning analytics
  3. To reflect on the ethical implications of the use of students’ data and the pitfalls linked to a naive conception of learning analytics
  4. To reflect on the value of data activism concerning learning analytics and data in education as a means to build “fair” data cultures in higher education

Introductory Media

Will the future of Higher Education be evidence-based? -Paul Prinsloo UNISA

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  • Data activism a social practice based on data technologies and infrastructures to contest the forms of power that are contained in struggles for social and ethical justice. It is a type of activism made possible as well as delimited by the evolution of datafication and data-driven practices based on extractive techniques
  • Learning analytics This term is known as the result of the measurement, collection, analysis and reporting of data on students and their learning contexts, in order to understand and optimize the learning process and the environments in which this happens.
  • Evidenced-based education Evidence-based education (EBE) - is a principle by which any educator and educational policy maker should seek the best scientific evidence or proof, rather than adopting tradition, educators' judgment, or other influences, to guide educational processes and practices. This principle is related to others such as evidence-based teaching, evidence-based learning, or evidence-based school effectiveness. The development of learning analytics has been added to this movement as a source of the “best evidence” considering the massive and extractive logic on which they are based, apparently overcoming the biases and problems of the experimental or observational methods of educational research
  • Data culture A set of practices and narratives that contextualise the approaches and perceptions that participants of an institution have about data and its use in that specific context
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Recommended Readings

  1. Broughan, C., & Prinsloo, P. (2020). (Re) centring students in learning analytics: in conversation with Paulo Freire. Assessment and Evaluation in Higher Education, 45 (4), 617–628. https://doi.org/10.1080/02602938.2019.1679716
  2. Ebbeler, J., Poortman, CL, Schildkamp, ​​K., & Pieters, JM (2016). Effects of a data use intervention on educators' use of knowledge and skills. Studies in Educational Evaluation, 48, 19–31 https://doi.org/10.1016/j.stueduc.2015.11.002
  3. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4 (5–6), 304–317 https://doi.org/10.1504/IJTEL.2012.051816
  4. Ferguson, R. (2019). Ethical challenges for learning analytics. Journal of Learning Analytics, 6 (3), 25–30 https://doi.org/10.18608/jla.2019.63.5
  5. High-Level Expert Group on AI. (2019). Ethical guidelines for Trustworthy AI. Brussels https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
  6. Milan, S., & van der Velden, L. (2016). The Alternative Epistemologies of Data Activism https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2850470
  7. Nunn, S., Avella, JT, 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
  8. Raffaghelli, JE (2018). Educators' Data Literacy Supporting critical perspectives in the context of a “datafied” education. In M. Ranieri, L. Menichetti, & M. Kashny-Borges (Eds.), Teacher education & training on ICT between Europe and Latin America (pp. 91–109). https://doi.org/10.4399/97888255210238
  9. Raffaghelli, JE, 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 Technologies in Higher Education, 17 (58) https://doi.org/https://doi.org/10.1186/s41239-020-00235-w
  10. Raffaghelli, JE, & 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
  11. Shum, SJB (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
  12. Siemens, G. (2013). Learning Analytics. American Behavioral Scientist, 57 (10), 1380–1400 https://doi.org/10.1177/0002764213498851
  13. Slade, S., & Prinsloo, P. (2013). Learning Analytics, Ethical Issues and Dilemmas. American Behavioral Scientist, 57 (10), 1510–1529 https://doi.org/10.1177/0002764213479366
  14. Stewart, B., & Raffaghelli, JE (2020). Why should we care about datafication? Critical data literacies in Higher Education | Zenodo. Barcelona https://doi.org/http://doi.org/10.5281/zenodo.3744135
  15. 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). New York, New York, USA: ACM Press https://doi.org/10.1145/3027385.3027400
  16. Vuorikari, R., Ferguson, Rebecca., Brasher, Andrew., Clow, Doug., Cooper, Adam., Hillaire, Garron., Mittelmeier, J., & Rienties, Bart . (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
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Key Complementary Resources

  1. Bonnie Stewart (2019) The ProSocial Web: Why Open Digital Practices Matter in the context of datafication - Webinar Series Fair Data Cultures in Higher Education (UOC-UWINDSOR) https://youtu.be/4RXAvHe0Mq0
  2. Regina Motz, Patricia Díaz (2020) - Fair learning analytics: design, participation and transdiscipline within the technostructure - Webinar Series Fair Data Cultures in Higher Education (UOC-UDELAR) https://youtu.be/O2QgvvcIXH0
  3. Paul Prinsloo - Will the future of Higher Education be evidence-based? - Paul Prinsloo UNISA - Lecture - UOC UNESCO CHAIR IN EDUCATIONAL TECHNOLOGIES FOR SOCIAL TRANSFORMATION https://www.youtube.com/watch?v=UK7flnbzZ4c
  4. Raffaghelli, J.E. (2021) El sentido de los datos en el ecosistema educativo. Serie “Educar con Sentido” Edición 2021, Eds. Rivera-Vargas, P., Miño, R., Passeron, E., Faro Digital & Grupo de Investigación Esbrina (Universitat de Barcelona) [Only Spanish] https://youtu.be/Y9xuGSx4cuA
  5. Adell, J. (n.d.). Seminario ‘Analíticas del aprendizaje: Una perspectiva crítica’ | CENT [Only Spanish] https://cent.uji.es/pub/jordi-adell-analitica-aprendizaje
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