data Review Balancing Plurality and Educational Essence: Higher Education Between Data-Competent Professionals and Data Self-Empowered Citizens Nils Hachmeister * , Katharina Weiß, Juliane Theiß and Reinhold Decker BiCDaS, Bielefeld University, 33501 Bielefeld, Germany; [email protected] (K.W.); [email protected] (J.T.); [email protected] (R.D.) * Correspondence: [email protected] or [email protected]; Tel.: +49-521-106-67599 Abstract: Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their Citation: Hachmeister, N.; Weiß, K.; emergence as fields of study, while additionally being stirred up by rapid developments, bringing Theiß, J.; Decker, R. Balancing about a need for flexibility and dialectic. Plurality and Educational Essence: Higher Education Between Keywords: data science; data literacy; higher education; curricula; competencies Data-Competent Professionals and Data Self-Empowered Citizens. Data 2021, 6, 10. https://doi.org/ 10.3390/data6020010 1. Introduction Received: 14 October 2020 Data are of the utmost importance throughout most facets of life, from academia [1–6], Accepted: 15 January 2021 to politics [7,8], to the economy [9,10], to our daily lives [11]: it is generated by our cars, Published: 21 January 2021 heating, fitness devices and communication. Its importance is still increasing as the costs of data generation are continuously dropping [12] and data covers more and more facets of Publisher’s Note: MDPI stays neu- the modern world. Equally important, sharing data, i.e., copying and transferring data, tral with regard to jurisdictional clai- has become effortless and low-cost, even becoming cheaper at an exponential rate [13]. ms in published maps and institutio- Furthermore, our societies are increasingly demanding data. Any claim, reasoning, decision nal affiliations. or political measure is perceived to be more convincing if it is grounded in data.1 This notion is exemplified by W. Edwards Deming’s quote: “In God we trust, all others must bring data.” Or, as Koltay et al. put it, “There is an aura of truth, objectivity, and accuracy around it [...]” [14]. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. Koltay and colleagues later on warn against falling prey to overly optimistic (or pes- This article is an open access article simistic) expectations regarding data. Objectivity, truth and accuracy (or their opposites) distributed under the terms and con- are attributes to be applied to a given analysis of data, not to the data itself. Notwithstand- ditions of the Creative Commons At- ing, errors can be made already in data collection, which hampers the expressiveness of tribution (CC BY) license (https:// any analysis conducted on such data. creativecommons.org/licenses/by/ That is not to say that the increasing importance of data is unjustified. The “datafi- 4.0/). cation” of our world is a powerful means of controlling, maintaining and improving that 1 Or credibly appears to be grounded in data. Data 2021, 6, 10. https://doi.org/10.3390/data6020010 https://www.mdpi.com/journal/data Data 2021, 6, 10 2 of 15 same world. However, we can only make the most of data when its potential and lim- itations and correct handling, analysis and interpretation are known. Known in every detail by a few experts excelling in data handling, but also known, on a less detailed level, by the general public. The importance data can have in public debate can be ob- served in many countries in Europe and elsewhere, in particular in the collective actions taken to counter SARS-CoV-2 [15,16]. This debate was centred around different statistical indicators, e.g., newly infected, deaths, hospitalisation and intensive care. At the begin- ning of the pandemic, discussion in public media was often on raw, absolute numbers of limited expressiveness, but then matured to more useful figures such as reproduction rates (R-value), normalised numbers and the statistical expressiveness of such figures became a subject of debate, examples for sources which attracted larger public interest in Germany and Europe are an interactive coronavirus map by the weekly newspaper Die Zeit [17], the Podcast Coronavirus-Update [18] or the COVID-19 Dashboard by CSSE, Johns Hopkins University [19]. The role data plays in fighting this pandemic, self-evident to any epidemiologist, has increasingly become a matter of common sense in public debates as well and is comprehensively summarised in Letouzé et al. [20]. Of course, data are being used for good and for ill during this debate, as many ill-formed statistics and data visualisations (intentional or not) were seen [21–23]. Accordingly, our educational systems need to adapt to these challenges, today more than ever. Curricula need to be developed, revised and adapted, modes of teaching data competencies need to be established, evaluated and enhanced. While this paper focuses on academic/higher education, it is the conviction of the authors that the teaching of data competencies needs to start much earlier, i.e., at the school level, in an adequately audience-tailored manner [24,25]. Therefore, teachers constitute an important group to be addressed by data literacy programmes. Data literacy programmes and publications that already address teachers—specifically or among other groups—focus on two different main areas namely (1) preparing teachers for teaching data competencies to students directly, e.g., [26,27], Bowen and Bartley (2014), as cited in [28], or (2) enabling teachers to improve their teaching by data-driven decisions [29,30]. Thus, teachers can act, directly or indirectly, as multipliers for data competencies and, even more importantly, for a general data awareness and data culture, directly by teaching data competencies to students within and across different disciplines and indirectly by data-driven guidance of teaching. Furthermore, it is important to bear in mind that due to differences in educational systems, curricula development for data competencies differs around the world. In conse- quence, academic discourses are at least partly separated, as for instance between European countries and the US. Due to the authors’ backgrounds, in the present paper, we will put an emphasis on the discussion in Europe, especially in Germany, while also referenc- ing discourses in other countries, especially the US and Canada, and pointing out some noteworthy differences. Academic education differentiates between disciplines and degrees (it is audience- tailored, if you will). It is governed by (an abstract understanding of) the requirements of the professional worlds students will eventually join. For the same reasons and by the same factors, education in data competencies needs to be differentiated. Obviously, a historian will need different data competencies2 than an astrophysicist. Teachers, as multipliers to the next generation, have received focus in this debate, particularly in the US [29–31]. In the area of teaching and leadership in education, four different roles for data experts that highlight different aspects of data expertise in the context of the US educational system have been defined: (1) practitioner administrator, (2) educational quantitative analyst, (3) research specialist and (4) data scientist [32]. Each of these four types would need its own, tailored set of data competencies. In Germany and other European countries, where the educational system is much less driven by standardised assessments and their results (that is data), these roles do not have a one-to-one correspondence. 2 And it has only recently been recognised in the first place by a wider community that data competencies might come in handy to historians. Data 2021, 6, 10 3 of 15 The distinction between data science and data literacy education has been broadly discussed in recent years. Both are active topics of academic discussions and curricula de- velopment around the world (e.g., in Germany [15,33,34], in Canada [35] or the US [29,30]). The distinction between the two is often clarified by the analogy of elite sports (≈data science education) and mass sports (≈data literacy education).3 Like every analogy, this has some shortcomings; for example, mass sports rarely has a professional dimension, while for data literacy a fundamental assumption is that it is needed in the professional world. It also implies seeing data literacy as a ‘minor