Educators’ understandings of digital classroom tools and datafication: perceptions from higher education faculty

Keywords: datafication, higher education, data, datafied systems, technology in education


Research has shown that critical data literacies development for educators is seldom a core component of most campus conversations about datafication, even as extractive, datafied systems become pervasive throughout the higher education sector. This article outlines findings from an international, qualitative, Comparative Case Study (CCS) of university professionals teaching online during the COVID-19 pandemic. It overviews beliefs and barriers shaping educators’ responses to datafication and focuses specifically on their perceptions of faculty development opportunities related to digital classroom tools and to datafication more broadly. The article presents insights into how faculty understands higher education’s contemporary datafied infrastructure and highlights participants’ voices about faculty professional development and critical data literacies. Based on our findings, we recommend formal faculty development and broader professional learning conversations as a means of enhancing faculty awareness and agency within the higher education sector.


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Amundsen, C. et al. (2005). The what and why of faculty development in higher education: An in-depth review of the literature. AERA. Retrieved from

Andrejevic, M. (2012). Ubiquitous surveillance. In D. Lyon, K. D. Haggerty, & K. Ball (Eds.), The Routledge Handbook of Surveillance Studies. Routledge, 1st ed., 91–98.

Atenas, J., Havemann, L. & Timmermann, C. (2020). Critical literacies for a datafied Society: Academic Development and Curriculum Design in higher education. Research in Learning Technology, 28, 2468.

Atenas, J., Havemann, L. & Timmermann, C. (2023). Reframing data ethics in research methods education: A pathway to critical data literacy. International Journal of Educational Technology in Higher Education, 20(1), 11.

Avella, J. T. et al. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13–29. Retrieved from

Bali, M. & Caines, A. (2018). A call for promoting ownership, equity, and agency in faculty development via connected learning. International Journal of Educational Technology in Higher Education, 15(1), 46.

Bartlett, L. & Vavrus, F. (2017). Comparative case studies: An innovative approach. Nordic Journal of Comparative and International Education (NJCIE), 1(1), 5–17.

Bashir, A. et al. (2021). Post-COVID-19 adaptations; the shifts towards online learning, hybrid course delivery and the implications for biosciences courses in the higher education setting. Frontiers in Education, 6, 711619. Retrieved from

Brand, J. & Sander, I. (2020). Critical data literacy tools for advancing data justice: A guidebook. Data Justice Lab. Retrieved from

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Braun, V. & Clarke, V. (2012). Thematic analysis. In APA handbook of research methods in psychology. American Psychological Association, 57–71. Retrieved from

Denzin, N. K. et al. (2017). Critical qualitative methodologies: Reconceptualizations and emergent construction. International Review of Qualitative Research, 10(4), 482–498.

De Simone, J. J. (2020). The roles of collaborative professional development, self-efficacy, and positive affect in encouraging educator data use to aid student learning. Teacher Development, 24(4), 443–465.

do Amaral, M. P. (2022). Comparative case studies: Methodological discussion. In S. Benasso et al. (Eds.), Landscapes of lifelong learning policies across Europe. Cham: Springer International Publishing, 41–60.

Ferguson, P. (2004). Faculty beliefs about teaching with technology. Association for Educational Communications and Technology. Retrieved from

Garraway, J. (2021). Academics’ learning in times of change: A change laboratory approach. Studies in Continuing Education, 43(2), 223–243.

Gratz, E. & Looney, L. (2020). Faculty resistance to change: An examination of motivators and barriers to teaching online in higher education. International Journal of Online Pedagogy and Course Design (IJOPCD), 10(1), 1–14.

Kiger, M. E. & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854.

King, N. (2004). Using templates in the thematic analysis of text. In C. Cassell & G. Symon (Eds.), Sage, 256–270. Retrieved from

Lloyd, S. A., Byrne, M. M. & McCoy, T. S. (2012). Faculty-perceived barriers of online education. Journal of Online Learning, 8(1). Retrieved from

Luongo, N. (2018). An examination of distance learning faculty satisfaction levels and self perceived barriers. Journal of Educators Online, 15(2). Retrieved from

Mandinach, E. B. & Gummer, E. S. (2016). Every teacher should succeed with data literacy. Phi Delta Kappan, 97(8). Retrieved from

Mozur, P., Mac, R., & Che, C. (2022). TikTok Browser Can Track Users’ Keystrokes, According to New Research. The New York Times. Retrieved from

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.

Persico, D. & Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry: Informing LD with LA to improve teacher inquiry. British Journal of Educational Technology, 46(2), 230–248. https://10.1111/bjet.12207

Pierson, M. E. (2001). Technology integration practice as a function of pedagogical expertise. Journal of Research on Computing in Education, 33(4), 413–430.

Raffaghelli, J. E. et al. (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.

Raffaghelli, J., Postigo, G. S. P. & Urviola, M. M. Z. (2021). Data-based practices in university teaching and lines of professional development: The case of Universidad Nacional de San Agustín, Arequipa. In Innovation experiences in tertiary education in Latin America, 13–28. Editorial Board. Retrieved from

Rapanta, C. et al. (2020). Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Science and Education, 2(3), 923–945.

Rodés, V. et al. (2021). Teacher education in the emergency: A MOOC-inspired teacher professional development strategy grounded in critical digital pedagogy and pedagogy of care. Journal of Interactive Media in Education, 2021(1), 12.

Schofield, M. (2021). Exploring datafication for teaching and learning development: A higher education perspective. In M. Ali & T. Wood-Harper (Eds.), Fostering communication and learning with underutilized technologies in higher education. Hershey, PA: IGI Global, pp. 79–92.

Stewart, B., & Lyons, E. (2021). When the classroom becomes datafied: A baseline for building data ethics policy and data literacies across higher education. Italian Journal of Educational Technology, 29(2), 54–68.

Stewart, B. et al. (2023). Barriers and beliefs: A comparative case study of how university educators understand the datafication of higher education systems. International Journal of Educational Technology in Higher Education, 20(1).

Selwyn, N. & Gašević, D. (2020). The datafication of higher education: Discussing the promises and problems. Teaching in Higher Education, 25(4), 527–540.

Selwyn, N. et al. (2021). Digital technologies and the automation of education – Key questions and concerns. Postdigital Science and Education, 5(1), 15–24.

Shilova, M. (2017). Datafication concept: Definitions and examples. Retrieved from

Shin, M. & Hickey, K. (2021). Needs a little TLC: Examining college students’ emergency remote teaching and learning experiences during COVID-19. Journal of Further and Higher Education, 45(7), 973–986.

Southerton, C. (2022). Datafication. In L. A. Schintler & C. L. McNeely (Eds.), Encyclopedia of Big Data. Cham: Springer, 358–361.

Spilker, M., Prinsen, F. & Kalz, M. (2020). Valuing technology-enhanced academic conferences for continuing professional development. A systematic literature review. Professional Development in Education, 46(3), 482–499.

Stake, R. (2008). Qualitative case studies. Sage. Retrieved from

Szcyrek, S., & Stewart, B. (2022). Surveillance in the system: Data as critical change in higher education. The Open/Technology in Education, Society, and Scholarship Association Journal, 2(2), 1–20.

Tsai, Y.-S. & Gasevic, D. (2017, March 13). Learning analytics in higher education – Challenges and policies: A review of eight learning analytics policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, BC, LAK’17, Association for Computing Machinery, 233–242.

Wasson, B., Hansen, C. & Netteland, G. (2016). Data literacy and use for learning when using learning analytics for learners. In LAL@LAK. Retrieved from

Wells, J. (2016). Academics do want new UNI business models: Open Universities official. In Campus review. Retrieved from

Williamson, B. (2015). Governing software: Networks, databases and algorithmic power in the digital governance of public education. Learning, Media and Technology, 40(1), 83–105.

Williamson, B., Bayne, S. & Shay, S. (2020a). The datafication of teaching in Higher Education: Critical issues and perspectives. Teaching in Higher Education, 25(4), 351–365.

Williamson, B., Eynon, R. & Potter, J. (2020b). Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45(2), 107–114.
How to Cite
Szcyrek S., Stewart B., & Miklas E. (2024). Educators’ understandings of digital classroom tools and datafication: perceptions from higher education faculty. Research in Learning Technology, 32.
Original Research Articles