Recent decades have witnessed a significant global expansion of datafication and digitalization, with far-reaching implications for the education sector. Digital data and technologies, as well as new intermediary actors promoting these technologies, have hereby become increasingly relevant in school policy, governance, and practice (Landri 2018, Williamson 2017), resulting in a substantial (re)configuration of education, schooling, and teaching professionality. At the same time, policy-making itself is increasingly becoming digital (e.g. on Twitter or other platforms) (e.g. Malin & Lubienski 2015; Jörgens et al. 2016).

Germany provides an interesting, yet still under-researched case to study the global transformation towards digital education governance, which is characterized by a significant reconfiguration of traditional (state) actors, the proliferation of a multi-faceted involvement of business interests (particularly EdTech), as well as the emergence of new intermediary actors and policy networks (e.g. Williamson et al. 2019, Landri 2018). At the same time, the multidimensional entanglement of traditional and new actors conjoined in the enactment of digital education governance, pose a substantial challenge for critical analysis also in the German case (e.g. Hartong & Förschler 2020). As addressed in newer lines of policy mobility research (Gulson et al. 2017, Williamson et al. 2019), analyzing contemporary policy-making thus needs to “adopt a ‘spatial’ research disposition and methodological approach” (Gulson et al. 2017, p. 236), focusing rather on relations than ‘positions’ within and across policy mobilities and networks (ibid., p. 226).

Our contribution focuses on these challenges by exploring new methodological ways (“inventive methods”, ibid.) to capture and analyze the dynamic and (growing) complexity of policy-making in digital education governance – the “Digital Agenda” in Germany.

We hereby combine qualitativenetwork ethnography (NE) (Förschler 2018) as a specific form of critical policy network analysis (e.g. Ball 2017, Hogan et al. 2016, Player-Koro 2019) on the one hand, and quantitative social network analysis (SNA) based on Twitter data (Schuster & Kolleck under review, see also “methods”) as a specific form of social media analysis (e.g. Malin & Lubienski 2015, Schuster et al. 2021).
As we seek to show, the combination of both methodological approaches as an inventive analytical framework reveals great potential to become part of a methodological toolbox suitable for researching complex transformations and actor constellations in digital education policy. It not only combines qualitative and quantitative approaches to explore policy-making, but also an integration of digital and analogue policy space.

This contribution brings together a qualitative network ethnography (NE) (e.g. Ball 2017, Hogan et al. 2016, Player-Koro 2019) and a quantitative SNA of Twitter data (e.g. Jörgens et al. 2016, Supovitz et al. 2018, Schuster et al. 2021) to explore the complex and multi-faceted actor networks in the context of the “Digital Agenda” in Germany. NE emphasizes the need to trace the relationality and mobility of policy-making to “understand new educational governance structures and interactions between education policy actors across (…) various sectors” (Hogan et al. 2016, p. 247). The starting point of the analysis can be a specific policy issue – in this case the “Digital Agenda” around the digitalization of schooling in Germany. It involves a combination of “mapping, visiting, and questioning” (Ball 2017, p. 32) via internet searches, interviews, document analysis as well as the analytical technique of constructing network diagrams (Ball 2017, Hogan et al. 2016). Consequently, the findings for this contribution build from analyzing a wide corpus including documents from intensive online research, transcripts of conducted expert interviews, and protocols of observations from participating in key events (“moments of meetingness”, Ball 2017, p. 35). In this way, NE captures actors and processes in both an analogue and digital context. The analysis of Twitter data has become increasingly relevant in policy research. Due to the nature of the platform, it represents the most relevant online social network for political communication (Supovitz et al. 2018). Hence, this approach focuses on actors and processes within digital education policy-making. On Twitter, relations to other users can be established by mentions, retweets and replies, creating communication networks around specific topics (e.g. digitalization in education). For this contribution, techniques of SNA are applied to map the relations between different actors and identify key actors in the network (Borgatti et al. 2018). SNA focuses on the relationships between actors as well as the structural properties of networks and can succeed in measuring hidden forms of influence (Jörgens et al. 2016). To identify the respective strengths and advantages, as well as limitations of each approach, we first compared the findings regarding the identified networks and their most central actors. Then, results visible in both studies, as well as those complementing each other, were evaluated with the aim of contributing to a toolbox suitable to study the multiple contexts of and the complex actor constellations in digital education policy.

Findings of both methods – NE and SNA – clearly show a broad variety of state and non-state actors participating in the “Digital Agenda” in Germany, with an increasing involvement of EdTech businesses and consultants in particular (see Förschler 2018, Hartong & Förschler 2020, Schuster & Kolleck under review). Tentative results indicate that both approaches benefit from being combined, revealing ‘blind spots’ and limited foci of each method as well as highlighting key findings visible in both approaches. The analysis of Twitter data using SNA allows us to include a wide range of different actors that are difficult to capture and provide clearly defined parameters to assess their influence. However, the use of Twitter is still limited to a specific group of actors operating in Twitter’s digital context, while other potentially influential actors remain in traditional, that is, analogue arenas. In contrast, NE focuses on larger organizations and their representatives and can provide an in-depth examination of political developments and processes in analogue and digital contexts. At the same time, the researcher in NE becomes a part of the investigated field, which can lead to a loss of distance over time. Combining the two approaches can address these limitations by, on the one hand, supporting and maybe adjusting the findings from the NE through the Twitter SNA and, on the other hand, using the findings from the NE approach to perform a more informed Twitter search based on pre-defined sets of actors or particularly important keywords. In conclusion, the presented analytical framework can be fruitful in addressing methodological challenges, such as: How to trace and analyze complex and ‘indirect’ entanglements of various actors in (digital) education policy-making? How to get hold of a dynamic field? How to consider both analogue and digital spaces as well as differing contexts of policy-making?

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