Conference Agenda

This symposium focuses on new forms of value in digital education and the role of the educational technology (Edtech) industry in constructing these forms. Edtech is fast evolving, which we can notice from the fast-rising number of established companies over the past decade (Komljenovic et al., 2021), increasing investment, particularly venture capital investment, in Edtech start-ups (HolonIQ, 2022), rising number of Edtech unicorns, i.e. companies valued at more than $1 billion (Brighteye Ventures, 2022), and increase in acquisitions in the industry (ibid).

Edtech products and services are already adopted by schools and universities as institutional users, as well as students and parents as individuals (Hartong & Decuypere, 2023). Edtech is also increasingly prominent in and supported by supra-national, national and institutional policies around the world (Williamson and Hogan, 2020). Emerging critical research finds that Edtech structures teaching and learning processes and determines how education is governed (Decuypere et al., 2021). It, therefore, matters what kind of Edtech is being innovated and rolled out into education. However, surprisingly, critical research is still scarce to study Edtech, its actors, practices and processes (cf. Macgilchrist, 2021). This symposium tackles this research gap with a specific focus on value of and in Edtech – a crucial matter in any form of economic exchange, but thus far not often explicit object of critical educational research (Doganova, 2018).

Papers in this symposium collectively argue that we need new theoretical and conceptual tools to understand digital platforms, digital data and their governance in education. We do not satisfy ourselves with traditional macroeconomic lenses, such as neoliberalism (Baltodano, 2012) or commodification (Amaral et al., 2018), because they do not have explanatory power to explain and study new trends, such as the shifting nature and newly emerging forms of educational governance. Here we notice a shift in the realisation of value away from an exchange in the market (as with commodities where there is an exchange of ownership rights) towards paying economic rents (as with assets, for example, where digital users pay a subscription to access an app and the app owner keeps the ownership and control over it). The symposium introduces the theoretical lens of assetization (Birch and Muniesa, 2020) aligned with a broader research strand of valuation studies that study ‘valuation’ as a social practice, and value in general as “the outcome of a process of social work and the result of a wide range of activities (from production and combination to circulation and assessment) that aim at making things valuable” (Helgesson & Muniesa, 2013:6).

The symposium addresses key themes and trends of how value is constructed in or extracted from the education sector. First, the papers analyse new (human and non-human) actor constellations in the field of edtech, including Edtech start-ups, investors and market intelligence agencies. Second, the symposium unveils underlying mechanisms and tensions that arise with the emergence of these new actor types and Edtech services and products, such as injustice and racialisation, that arise in the wake of such new evolutions. Third, the symposium discusses dimensions of valuation, including temporality, in/stability, innovation and expectations. Fourth, we study various ways in which valuation creates new sorts of temporalities (past, present, and future). Finally, we present a variety of methodological approaches to studying value and Edtech (team ethnographies, database analyses, rapid interviews, document analysis) and research sites (trade fairs, news, Internet sources, databases). In doing so, the symposium at once systematizes our knowledge of how value is being produced, extracted, and ‘done’ in and by the edtech sector and aims to give new impetus to better understanding the specificity of the edtech sector as a whole.

This paper is situated in the field of critical edtech studies that aim to gain nuanced empirical understanding of recent developments in the edtech scene (Decuypere & Hartong, 2022; Macgilchrist, 2021). One example hereof is the edtech start-up sector; a sector that has thus far hardly received any scholarly attention (Williamson & Komljenovic, 2022; Tripathi et al., 2019). Seeking to obtain a more profound understanding of this sector, this paper presents a team ethnography conducted with an international team of 8 researchers at the end of 2022, in a trade fair that was exclusively focused on edtech start-ups. Our main research interest is what makes the edtech start-up sector a specific sector distinguishable from the general edtech sector, as well as how it seeks to create and extract value out of the educational sector. As such, our contribution seeks to contribute to emerging scholarship on the importance of analysing trade fairs in education (e.g., Player-Koro et al., 2022; Gulson & Witzenberger, 2022). Analytically, our analysis is focused on the discursive construction of value through stories, rhetorical constructs (e.g., ‘unicorns’; ‘ecosystems), and sociomaterial artefacts present in the trade fair (ibid.; Birch, 2022). The preliminary results of our analysis indicate different areas of value creation. First, even though the edtech start-up sector strives for global reach, we can at the same time discern a very localised ‘geography of tech production’, in which the hosting city of the fair seeks to extract value for the city itself as ‘innovation complex’ (cf. Zukin, 2020). Second, we see that venture capital is made central in the education sector, by means of projecting future value into the present (cf. Birch, 2022). Third, we discern the rising importance of ‘meta-organisations’ that create value for the edtech start-up sector by adopting intermediary positions between start-up corporations and schools. Fourth, and last, we can approach the fair itself as creating expert knowledge for start-ups: amongst all types of valuations, expert knowledge on the specificity of the educational sector – as a sector very distinct from traditional sectors to invest in – is the one that matters most. In conclusion, the paper argues that education is a very specific field, characterised by unique features (e.g., harder to ‘access’ and ‘scale’) that need to be taken into account if we want to fully edtech start-ups as new actors in the edtech scene.

Birch, K. (2022). Reflexive expectations in innovation financing: An analysis of venture capital as a mode of valuation. Social Studies of Science, 03063127221118372. Decuypere, M., & Hartong, S. (2022). Edunudge. Learning, Media and Technology, 1-15. Gulson, K. N., & Witzenberger, K. (2022). Repackaging authority: artificial intelligence, automated governance and education trade shows. Journal of Education Policy, 37(1), 145-160. Macgilchrist, F. (2021). What is ‘critical’ in critical studies of edtech? Three responses. Learning, Media and Technology, 46(3), 243-249. Player-Koro, C., Jobér, A., & Bergviken Rensfeldt, A. (2022). De-politicised effects with networked governance? An event ethnography study on education trade fairs. Ethnography and Education, 17(1), 1-16. Tripathi, N., Seppänen, P., Boominathan, G., Oivo, M., & Liukkunen, K. (2019). Insights into start-up ecosystems through exploration of multi-vocal literature. Information and Software Technology, 105, 56-77. Williamson, B., & Komljenovic, J. (2022). Investing in imagined digital futures: the techno-financial ‘futuring’ of edtech investors in higher education. Critical Studies in Education, 1-16. Zukin, S. (2020). Seeing like a city: how tech became urban. Theory and society, 49(5), 941-964.

This paper argues that the racialised extraction of value is central to education technology that uses forms of artificial intelligence. Rather than suggest that racialisation is a problem to be ameliorated in EdTech, this paper contends that racialisation is essential to both the operation of AI supported EdTech, and its capacity to garner market share. To make this argument the paper has two parts. The first part of the paper outlines the centrality of racialisation to the operation of AI supported Edtech, focusing on two areas that underpin systems that use facial recognition technologies. The first area examines the links between race and training sets, including issues of exclusion and misrecognition of people of colour (Crawford & Paglen, 2021). This is now a common focus where addressing bias is seen as a key remedy for racial bias. Conversely, this paper draws on work that highlights that including people of colour in training sets can create more accurate systems, but not less pernicious ones as inclusion can be deleterious for historically marginalised and surveilled populations (Benjamin, 2019). The second area is algorithmic. While most focus on the links between race and technology are on data, there is an important but underexamined dimension in the historical racialisation of machine learning methods and algorithms. This includes machine learning methods used in common AI technologies in EdTech such as facial recognition. For example, facial recognition technologies use the Mahalanobis similarity measure which has racial origins in colonial rule in India (Taylor, Gulson, & McDuie‐Ra, 2021). The second part of the paper focuses on the notion of racialised extraction of value, drawing on critical theories of race and technology, including those related to racial capitalism (McMillan Cottom, 2020). This notion of racial capitalism provides insights in this paper to the way education technology derives both social and economic value through racialised data and algorithmic practices (e.g., Henne, Shelby, & Harb, 2021). In education technology this can include the production of market value, simultaneous with the production of allocative and representational harms, such as racial profiling while using education platforms (Nichols & Garcia, 2022). This paper concludes by contending that the racialised extraction of value is both necessary for including people of colour in Edtech (e.g., being able to use the products in the markets of the majority, non-White world), and yet also reinforces the historical and pernicious surveillance of people of colour in education.

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Cambridge: Polity. Crawford, K., & Paglen, T. (2021). Excavating AI: the politics of images in machine learning training sets. AI & SOCIETY, 36(4), 1105-1116. doi:10.1007/s00146-021-01162-8 Henne, K., Shelby, R., & Harb, J. (2021). The Datafication of #MeToo: Whiteness, Racial Capitalism, and Anti-Violence Technologies. Big Data & Society, 8(2), 20539517211055898. doi:10.1177/20539517211055898 McMillan Cottom, T. (2020). Where Platform Capitalism and Racial Capitalism Meet: The Sociology of Race and Racism in the Digital Society. Sociology of Race and Ethnicity, 6(4), 441-449. doi:10.1177/2332649220949473 Nichols, T. P., & Garcia, A. (2022). Platform Studies in Education. Harvard Educational Review, 92(2), 209-230. doi:10.17763/1943-5045-92.2.209 Taylor, S., M., Gulson, K. N., & McDuie‐Ra, D. (2021). Artificial Intelligence from Colonial India: Race, Statistics, and Facial Recognition in the Global South. Science, Technology & Human Values, 1-27. doi:10.1177/01622439211060839

The educational technology (EdTech) industry as we know it today is relatively young and developed in the early 2010s when the number of newly established Edtech companies started sharply increasing (Komljenovic et al., 2021) and Venture capital (VC) investment in Edtech rising from $500 million in 2010 to more than $20 billion in 2021 (HolonIQ, 2022). The industry is now consolidating, as indicated by the rising value of individual investments into particular companies (Brighteye Ventures, 2022), a rising number of acquisitions (Brighteye Ventures, 2022), and the emergence of ‘Big Edtech’ (Williamson, 2022). EdTech is increasingly prominent in HE by structuring teaching and learning processes, determining how education is governed, and reframing educational purposes (Decuypere et al., 2021). In such context, it matters what kind of EdTech is innovated and rolled out into the sector, and what kind of value it brings to its key actors. This paper focuses on three key stakeholders in EdTech, namely universities, EdTech companies, and investors in EdTech. It presents some of the results from a larger ESRC-funded project ‘Universities and unicorns’ (Komljenovic et al., 2021), that collected and analysed more than 2,000 documents and more than 50 interviews with university, company and investor leaders. It argues that there are discrepancies and tensions in how these three actors perceive and construct value of EdTech in HE. For investors, EdTech is an investment category that should bring a return on investment. For companies, EdTech brings specific niche benefits, mostly focusing on institutional efficiencies, and automation and personalisation of learning. Universities value EdTech as a means for boosting recruitment, reputation, and student experience, as well as a potential source of organisational and pedagogical innovation. Some of the key tensions between these orientations are temporality (investors, especially venture capital, want rapid scale and fast returns; while universities work on longer cycles), stability (start-ups might aim to be sold to another company or change price and service in the near future, but universities need stability and longevity), nature of innovation (who participates in innovating and what are the pedagogical and ethical premises), and expectations (investor and company discourse promotes ideas of data-rich operations such as AI, but universities experience only unsophisticated EdTech products with basic feedback loops at best). For EdTech to be useful for students, staff and other HE actors, and sustainable, these discrepancies should be addressed.

Brighteye Ventures. (2022). The European Edtech Funding Report 2022. https://docsend.com/view/jz3gpvpdibmqqqt7 Decuypere, M., Grimaldi, E., & Landri, P. (2021). Critical studies of digital education platforms. Critical Studies in Education, 62(1), 1–16. HolonIQ: https://www.holoniq.com/notes/global-Edtech-venture-capital-report-full-year-2021 Komljenovic, J. (2021). The rise of education rentiers: Digital platforms, digital data and rents. Learning, Media and Technology, 1–13. https://doi.org/10.1080/17439884.2021.1891422 Komljenovic, J., Sellar, S., & Birch, K. (2021). Mapping Emerging Edtech Trends in the Higher Education Sector: Companies , Investment Deals & Investors. Universities and Unicorns project Report 2 of 4 (Issue November). https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/universities-and-unicorns/UU-Phase1-Quant-Report2of4-final.pdf Williamson, B. (2022). Big EdTech. Learning, Media and Technology, 47(2), 157–162. https://doi.org/10.1080/17439884.2022.2063888 Williamson, B., & Hogan, A. (2020). Commercialisation and privatisation in/of education in the context of Covid-19 (Issue July). Education International.

The future of education is a site of struggle and contestation among a vast variety of actors, organisations and interests (Robertson 2022). Dominant visions of education futures are often framed by technocratic elites that can mobilise ideational and material resources and anticipatory expertise to delineate authoritative scenarios and visions (Facer & Sriprakash 2021). Educational futures are currently being inscribed in discursive visions of ‘digital transformation’ by political, industry and financial actors (Clark 2023), furthermore underpinned by quantitative valuation claims about the prospective economic returns from educational technology (edtech) investment (Williamson & Komljenovic 2022). The specific methods of market prediction used to calculate future edtech value, how these valuation methods inform visions of digital educational transformation, and the promissory politics that infuse such efforts are the original focus of this paper. Sociological work on expectations and futures examines the practices and political contests through which ‘promissory authorities’ construct and circulate visions (Tutton 2017). ‘Techniques of futuring’ refer to the practical ways selected futures are constructed, attract interest, and foster investments as ‘authoritative orientations for action’ (Oomen et al 2022). Futuring techniques span from creative and imaginative practices to calculative, rational and scientific methodologies like predictive forecasting and modelling, and are part of a ‘politics of expectation’ (Beckert 2016). Our contribution conceptualises ‘algorithmic futuring’ as the design and use of data-driven technologies and methods to predict educational futures and animate actions in the present towards their materialisation. Algorithmic futuring is part of a contemporary tendency to apply predictive techniques to societal problems, materialised in ‘technologies of speculation’ through which ‘social problems are made conceivable only as objects of calculative control’ (Hong 2022). We identify and examine promissory authorities where futures are constructed through algorithmic futuring methodologies in terms of edtech value, including management consultancies, think tanks and market intelligence agencies. Their algorithmic futuring methods include edtech market prediction with machine learning, natural language processing and clustering algorithms, aimed at directing venture capital investments towards high-value yields; and data-scientific predictions constructed by think tanks and consultancies to convince politicians and policymakers to invest in digital education as a route to long-term economic value. Algorithmic futuring constitutes a methodological practice that combines technologies of speculation with calculative and predictive practices of valuation. It functions to delimit the desirability of educational futures in terms of prospective future edtech value, exemplifying the speculative technologies, methods and promissory politics involved in performing predictive futures into existence.

Beckert, J. (2016) Imagined futures: Fictional expectations and capitalist dynamics. Harvard University Press. Clark, D. (2023) The construction of legitimacy: a critical discourse analysis of the rhetoric of educational technology in post-pandemic higher education. Learning, Media and Technology, DOI: 10.1080/17439884.2022.2163500. Facer, K. & Sriprakash, A. (2021) Provincialising Futures Literacy: A caution against codification. Futures, 133, DOI: 10.1016/j.futures.2021.102807. Hong, S.-H. (2022) Predictions without futures. History and Theory, DOI: 10.1111/hith.12269. Oomen, J., Hoffman, J., & Hajer, M. A. (2022) Techniques of futuring: On how imagined futures become socially performative. European Journal of Social Theory, 25(2), 252–270. Robertson, S. (2022) Guardians of the Future: International Organisations, Anticipatory Governance and Education. Global Society, 36(2), 188-205. Tutton, R. (2017) Wicked futures: Meaning, matter and the sociology of the future. Sociological Review, 65(3), 478–492. Williamson, B. & Komljenovic, J. (2022) Investing in imagined digital futures: the techno-financial ‘futuring’ of edtech investors in higher education, Critical Studies in Education, DOI: 10.1080/17508487.2022.2081587.