The desire to optimize and control education by predicting the future plays an increasing role in contemporary educational governance. Today, economic forecasts of future labor market needs constitute one type of governance technology of importance for educational governance, and a technology of future-making (Wenzel, 2022). Through websites and reports, visual images of forecasted futures stage higher education in relation to a desired future and furthermore imply particular policy decisions to be rational and timely in order to achieve this future. In these visual images, the uncertainty of the future is often masked, as visual shapes of intervals and areas are replaced by lines and bars. Whereas lines and bars indicate an exact prediction, intervals and areas indicate a range of more or less probable future outcomes, which is more in line with the statistical forecasting methods developed by economics (Elliott & Timmermann, 2016). Based on two case studies, including a Danish and a Norwegian economic forecasting of labor market needs, and the methodological and analytical concept of topology (Allen, 2016; Decuypere, 2021; Decuypere, Hartong, & van de Oudeweetering, 2022), this paper explores different visual approaches to future-making in such technologies and the implications of these for the promotion of particular policy-driven concepts of the future of education and rational and timely policy decisions. The cases include forecasts of the future educational profiles of Norwegian graduates, produced by Statistisk Sentralbyrå in Norway (Statistisk Sentralbyrå, 2020), and forecasts of the future educational profiles of Danish graduates combined with forecasts of the labor market needs of the public sector in Denmark, produced by the Danish Ministry of Higher Education and Research (Committee on Better University Programs, 2018). These technologies are conceptualized as topological forms that via their ways of visually connecting data points describing the past with data points describing the future have particular temporalizing effects. They will be studied in terms of their architecture (Decuypere 2021), including how the different data points in each technology are generated, how they relate to each other, and how they are extended into the future, combined with studies of their interface (Decuypere 2021), including how this is visualized differently in forecasts disseminated in foreground policy texts and in background methodology texts. The analysis will furthermore draw on interviews with experts who have been involved in producing the forecasts. The findings will be analyzed in light of the higher education policy circumstances characterizing Denmark and Norway respectively.

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