In the last ten years digitalized data have permeated our lives in a massive way. Beyond the internet ubiquity and cultural change outlined in what Castells (1996) called the network society, we are now witnessing a datafied society, where large amounts of digital data—the DNA of information—are driving new social practices. The most enthusiastic discourses on this abundance of data have emphasized the opportunity to generate new business models, with professional landscapes connected to data science and open practices in science and the public space (EMC Education Services 2015; Scott 2014). However, more recently, the rather naïve logic of data capture and its articulation through various algorithms as drivers of more economical and objective social practices have been the object of criticism and deconstruction (Kitchin 2014; Zuboff 2019). 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 considered a form of innovation and laid the foundations for the later phenomenon of datafication (Williamson 2018). Initially, fervent discourses embraced data-driven practices as an opportunity to improve efficiency, objectivity, transparency and innovation (Daniel 2015; Siemens et al. 2013). The two main missions in higher education (HE)—teaching and research—went through several processes of digitalization that encompassed data-intensive practices. In teaching, the data about learning and learners collected on unprecedented scales gave rise to educational data mining and particularly to learning analytics (LA) (Siemens and Long 2011). While some argued about the value of learning analytics in informing teachers’ decision-making about pedagogical practices as well as learners’ self-regulation (Ferguson 2012; Roll and Winne 2015), research also uncovered naïve or even poor pedagogical assumptions on the power of algorithms to predict, support and address learning, which were connected to techno-determinist approaches to data (Ferguson 2019; Perrotta and Williamson 2018; Selwyn 2019). The studies in the field have pointed out how few connections there are between LA models and pedagogical theories (Knight et al. 2014; Nunn et al. 2016), the lack of evaluation in authentic contexts, the scant uptake by teachers and learners (Vuorikari et al. 2016a, b) and the social and ethical issues connected to the topic (Broughan and Prinsloo 2020; Slade and Prinsloo 2013; Prinsloo and Slade 2017). Moreover, the massive adoption of social media has crossed paths with learning management systems, creating new forms of data of which both teachers and students could be completely unaware (Manca et al. 2016).
In the aftermath of the HE pivot online and the resulting “pandemic pedagogies,” the problem of data usage and data ethics through the marketization of data and algorithms has emerged as a hidden consequence (Williamson et al. 2020). At the same time, the advancement of networked, open and pro-social research has increased data availability around the world (Bozkurt et al. 2020).
Judged against this complex framework, data literacy would appear to be an important skill to possess. Approaches such as that of D’Ignazio and Bhargava (2015) show the investigations made in education (in this case, adult education for civic participation) to generate agentic practices around datafication. Also, Pangrazio and Selwyn (2020) have investigated the ways HE and school students engage with personal data collected via social media and personal apps. Their design- and intervention-based research focused on improving understanding of the lack of transparency and monetization of data, but also uncovered passive attitudes among the students in the trade-off between data extraction and their usage of the digital environments surrounding them. In the case of HE, important reflections about the way students and teaching staff should engage with the academic and learning analytics systems yielded interesting considerations of the need for privacy by design, usability and engagement and transparency in students’ data usage (Jivet et al. 2020; Tsai and Gasevic 2017).
Analysis of data collection and visibility has highlighted another side of data practices, entailing positive connotations in contrast with the prior view of data usage as a form of surveillance. The paradigm of open science, which invited citizens to engage, explore and contribute to data collection processes in research, was deemed a powerful tool for innovating in science communication and a way of promoting informal learning (Owen et al. 2012). Moreover, scholarly practice might address new connections between research and teaching through the use of open data as open educational resources (Atenas et al. 2015), moving towards a widespread scientific culture. However, actual practices in HE reveal several issues regarding the implementation of these types of innovation (Raffaghelli 2018).
Based on the above, the reader might grasp the problem of a fragmented phenomenology relating to data epistemologies and the required literacies (Milan and van der Velden 2016). Unquestionably, there is an increasing number of research projects and studies in social sciences that address a critical perspective on the problem of data practices in general and in HE as one of the key institutions of our contemporary society. Discussion about the literacies required is also becoming a clear matter of concern. But the ways in which society and scholars characterize data practices vary considerably, and are based on different “data epistemologies” (in a continuum from positive and proactive to negative and reactive) that contextualize the various discourses.
However, the lack of awareness of the fragmentation in the phenomenology of data practices prevents educators and higher education institutions (HEI) from intervening to set policies or implement a professional praxis beyond a limited, externally driven focus on data instead of a contextualized vision of data. It is worth considering the concept of “data culture” at this point. A data culture is seen as a situated, collective expression which encompasses professional identities, policies and specific practices relating to data, as part of an institutional culture. As such, the awareness that actors (learners, the professoriate in both its teaching and research activities, staff, HEI management and even families supporting the students) have of the contextual and material characteristics of data imaginaries could potentially provide the basis for uncovering power issues, misrepresentation and inequities, and thereby pave the way to building fairer data practices. We must not forget that HE has been characterized by its commitment to advancing knowledge in society and, more recently, to promoting the development of capacities to thrive as creative and responsible citizens (Fikkema 2016; McAleese et al. 2013). In the case of datafication and all it entails, with the advancement of artificial intelligence and the Internet of Things as marketable innovations, the complex tension between the goals of a neo-humanistic perspective and the requirements of the technocracy (which has been a matter of discussion since the beginning of the university) has become even clearer. However, it is also clear that the role of the university is to blend advanced, interdisciplinary theoretical reflection with empirical research and practice in the field of datafication within a space of meaning-making (particularly university teaching). In such a space, as envisioned early on by Humboldt, academics and students engage in a conversation which ultimately pushes the latter to take an active part in addressing the problem of data practices and cultures as reflective citizens and professionals (Pritchard 2004). On these basis, the university is called on to mediate meaning-making through activities such as collaboratories, workshops, professional development and quality evaluation exercises in addition to actual research activities. These are spaces that ensure that the conceptualization and problematization of datafication are kept at the forefront of the agenda both within and beyond the university. Moreover, curriculum design, with its frameworks of competence promoting active and engaging pedagogical practices, acts as a sort of circle of positive reification of knowledge and entails intense reflection over the existing knowledge (and concepts) of datafication and data practices.
The main goal of this Special Section is to advance the discussion on data practices in HE towards constructing an agenda of critical reflection regarding the literacies required. We anticipated that the EdTech community may react in unexpected ways to the questions proposed in the call, which were intended to act merely as ice-breakers. Nonetheless, the empirical papers received could have been started one or two years before the submission of the final paper sent to our call.
Fairly predictably, a substantial group of contributions dealt with mainstreaming learning analytics and data literacy as a technical endeavour in HE. The studies finally included in this collection mostly focus on learning analytics as a way of informing pedagogical practices, and contain a certain degree of critical analysis of the design and deployment of such educational technology innovations.
As co-editors, we felt that in order to underpin the four papers included in this section as selected pieces we needed to present the puzzle of different perspectives on how HE contributes to the development of critical data literacies (Markham 2018; Tygel and Kirsch 2016) as a means of building fair data cultures. We decided that the presentation of this puzzle could take the form of a position paper outlining the steps we intended to take to address the complex phenomenology of data cultures and practices in HE. This task was based on the four areas of research in which the co-editors are involved, thereby providing a bigger, albeit incomplete, picture of how the four research papers selected could fit in. The order given to the critical perspectives described is based on the approach adopted to data practices observation, namely, moving from institutional strategies to professional skills to students’ literacies, and finally moving beyond the HE context. Although the four perspectives, together with the four selected articles, might not be entirely aligned in their critique approach to data epistemologies, they all converge in requesting a review and analysis of the social and educational impact of current data practices in HE.
Albert Sangrà provides the first perspective, having worked for over two decades in addressing the quality of online education in HE. Embracing a proactive data epistemology, he highlights the opportunity provided by data-driven approaches to analysing educational quality. At the same time, he unveils the criticalities of metrics and their meaning for the reputation of HEIs, disentangling the impacts of such instruments on both institutional culture and academics’ and students’ decisions and priorities. The second perspective, based on nearly ten years of research into the ethical concerns surrounding learning analytics, is that of Paul Prinsloo, who explores in depth the problems of students’ data and the usage of these data to produce a techno-structure for learning analytics. Prinsloo explores the problem through the conceptual lens of vulnerability as an inherent condition of students in the system. The third perspective, introduced by Bonnie Stewart, whose work also has a long tradition in the issues of professional digital identity and digital scholarship, builds on the need to construct critical data literacies to navigate data within the university and beyond, and the connected requirements of faculty development to achieve this goal. The fourth perspective, offered by Stefania Manca on the basis of her expertise in the field of informal learning and professional development through social media, relates to data usage “on the wildness” of social media beyond the university context. Her perspective embeds critical data literacies within social media literacy.
While there is no “one-to-one” relationship between the selected papers and these perspectives, the former sampled the need for data literacy among university staff to produce a common vision of quality in HE, taking into consideration the huge use of metrics in such an endeavour (Yang and Li 2020); they also addressed the complexities of privacy and data usage in the design of learning analytics (Cerro Martínez et al. 2020) and the criticalities of extracting text as data to characterize and analyse polemic constructs such as gender issues in students’ evaluation of practice (Okoye et al. 2020); and finally, they also explored a conceptual model for addressing educators’ data literacy to enable them to engage with teaching analytics through an informed and mindful approach (Ndukwe and Daniel 2020).
In the remainder of this paper, we will introduce the four perspectives followed by the contributions made by the four articles and proceed to discuss them. The conclusions draw on this rich synthesis of research work to build an idea of the literacies required to support the emerging fair data cultures in HE.