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Towards a critical perspective on data literacy in higher education. Emerging practices and challenges.

Digitalized data has entered our lives in a massive way in the last ten years. Beyond the Internet of the Information Society, we are now witnessing a datafied society, where large amounts of digital data, the DNA of information, are addressing new social practices. The most enthusiastic discourse on this abundance of data has emphasized the opportunity to generate new business models, new professional landscapes connected to the science of data, open practices in science and the public space (EMC Education Services, 2015; Scott, 2014). More recently, the rather naïve logic of data capture and articulation throughout several algorithms as drivers of more economical and objective social practices has been the object of criticism and deconstruction (Kitchin, 2015). The university as an institution fell into this paradigm somehow abruptly, while striving to survive its crisis of credibility. The digitalization of processes and services was hence considered a form of innovation and laid the foundations for the later phenomenon of datafication (Daniel, 2015). Initially fervent discourses embraced data-driven practices as an opportunity to improve efficiency, objectivity, transparency and innovation (Daniel, 2017). The two main missions in Higher Education, teaching and research, went through several processes of digitalization that encompassed data-intensive practices. From the side of teaching, the data collected about learning and learners on unprecedented scales, giving birth to educational data mining and particularly to learning analytics, which in turn led to techno-determinist visions of educational quality. While there is doubtless value in the developments proposed by learning analytics to support teachers’ pedagogical practices and learners self-regulation (Ferguson, 2012), there are frequent assumptions on the power of algorithms to predict, support or address learning that could prevent agentic and transformational practices if unsupervised (Perrotta & Williamson, 2018). The studies in the field have pointed out the scarce connections of LA models and pedagogical theories (Knight, Buckingham Shum, & Littleton, 2014; Nunn, Avella, Kanai, & Kebritchi, 2016), the lack of evaluation in authentic contexts, the difficult uptake by teachers and learners (Vuorikari et al., 2016), and the social and ethical issues connected to the topic (Prinsloo & Slade, 2017; Slade & Prinsloo, 2013). Moreover, the massive adoption of social media at the crossroads with learning management systems implies new forms of data from which both teachers and students could be completely unaware (Manca, Caviglione, & Raffaghelli, 2016). 

In terms of the current research, the paradigm of Open Science, which invites citizenship to explore and contribute with greater precision to the data collected by researchers, is generating an opportunity to innovate teaching and learning. The scholarly practice might address new connections between research and teaching through the use of Open Data as Open Educational Resources (Atenas, Havemann, & Priego, 2015), towards a widespread scientific culture. However, actual practices in Higher Education reveal several issues in implementing these types of innovation (Raffaghelli, 2018).
It seems that the appropriation of data in relevant ways requires adept reflective and critical skills, demonstrated in terms of  data literacies (Pangrazio & Selwyn, 2019; Wasson, Hansen, & Netteland, 2016). Specifically, in the case of academics, this could encourage  new forms of professionalism guiding teaching and learning in digital contexts. Adopting Boyer’s well-known concept of SOTL (scholarship of teaching and learning) data literacy could lead to more agentic forms of analyzing, evaluating, and sharing effective pedagogical practices. Moreover, it could cast a strategic vision of Open Education Science (Zee & Reich, 2018), as the datasets yielded from educational design-based research could be critically reviewed and shared in an broad educational community.

Hence, beyond a critical vision, data could be introduced into the classroom in creative and fair ways, informing teaching and learning and helping to ameliorate complex educational processes such as learning design or formative assessment; or by being utilized as authentic educational resources for learning.

However, while teachers’ data literacy in compulsory education has been a matter of consideration in the last fifteen years (Mandinach & Jimerson, 2016); and data literacy is deemed central as an academic and civic competence (Raffaghelli, 2018), there remains  a dearth of firsthand conceptual and empirical research supporting academic teachers’ professionalism; as well as guidance for students’ self-regulated learning to achieve data literacy in the context of the data-driven university. 

As such, this call for papers attempts to address a number of topics potentially connected with the research problem of data literacy for teaching and learning in Higher Education:

  • What type of data is collected in specific institutional cases, and what are the subsequent conceptual and pedagogical foundations required to process this data?
  • What problems of usability of data and data visualizations (i.e. learning analytics’ dashboards) have been observed along one or more cycles of authentic evaluation?
  • How are teachers addressing the pedagogical practices on the basis of the available data? How do they surf the data-abundance, across institutional and social contexts of digital learning?
  • How are students addressing their learning processes across the data-driven devices and resources? How do they surf the data-abundance, across institutional and social contexts of digital learning?
  • Is there critical awareness on the visibility and usage of institutionalized and social data?
  • What types of skills and abilities are required to search, analyze, adopt and share the data connected to teaching and learning processes in Higher Education?
  • How can the data and data interpretations be shared, to encourage open education science and open educational practices? 
  • On the basis of these general research questions, the editors are looking for case studies, reviews, reports on technological developments, outputs of research/studies, and examples of successful projects, as well as conceptual approaches, which document the current knowledge.

Submission Instructions
Before submitting your manuscript, please ensure you have carefully read the submission guidelines for International Journal of Educational Technology in Higher Education. The complete manuscript should be submitted through the International Journal of Educational Technology in Higher Education submission system. To ensure that you submit to the correct thematic series please select the appropriate thematic series in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the thematic series ‘Towards a critical perspective on data literacy in higher education. Emerging practices and challenges’. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Deadline for submissions:  
5 November 2019

Lead guest editor
Juliana Elisa Raffaghelli, Universitat Oberta de Catalunya - Estudis de Psicologia i Educació, Spain

Guest editors
Stefania Manca, Institute for Educational Technologies - National Research Council of Italy, Italy
Bonnie Stewart,  Faculty of Education, University of Windsor, Canada
Paul Prinsloo, College of Economic and Management Sciences, University of South Africa , Republic of South Africa​​​​​​​
Albert Sangrà, Universitat Oberta de Catalunya, Spain​​​​​​​

Submissions will also benefit from the usual advantages of open access publication:

Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient

High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article

No space constraints: Publishing online means unlimited space for figures, extensive data and video footage

Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed


Daniel, B. K. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920.

Daniel, B. K. (2017). Big Data in Higher Education: The Big Picture. In Big Data and Learning Analytics in Higher Education (pp. 19–28). Cham: Springer International Publishing.

EMC Education Services. (2015). Data Science & Big Data Analytics. Indianapolis, IN, USA: John Wiley & Sons, Inc.

Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317. Retrieved from Jan 2013.pdf

Kitchin, R. (2015). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (Vol. 1). London: Sage.

Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space. Journal of Learning Analytics, 1(2), 23–47.

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).

Mandinach, E. B., & Jimerson, J. B. (2016). Teachers learning how to use data: A synthesis of the issues and what is known. Teaching and Teacher Education, 60, 452–457.

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).

Pangrazio, L., & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2) 419–437.  

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.

Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference on - LAK ’17 (pp. 46–55). New York, New York, USA: ACM Press.

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). Genoa, Italy: European Distance and E-Learning Network.

Scott, A. (2014). Open data for economic growth. World Bank, 1–20. Retrieved from

Slade, S., & Prinsloo, P. (2013). Learning Analytics, Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

Vuorikari, R., Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., … Rienties, B. (2016). Research Evidence on the Use of Learning Analytics. Brussels.

Wasson, B., Hansen, C., & Netteland, G. (2016). Data Literacy and Use for Learning when using Learning Analytics for Learners. In S. Bull, B. M. Ginon, J. Kay, M. D. Kickmeier-Rust, & M. D. Johnson (Eds.), Learning Analytics for Learners, 2016 workshops at LAK (pp. 38–41). Edimburg: CEUR. Retrieved from

van der Zee, T., & Reich, J. (2018). Open Education Science. AERA Open, 4(3), 233285841878746.

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