Predictive Governance

Predictive governance tools are playing an increasingly important role in the context of the growing datafication and digitalization of education management. They include early warning indicator systems (EWIS), which are permanent learning analytics that aim at systematically reducing negative educational outcomes by intervening as early as possible. Data is used “[…] to identify students who are off track” (Bruce et al, 2011) and thus to generate the most solid future predictions possible.

At the professorship we investigate the design and effects of such early warning indicator systems with a focus on schools. Among other issues, we address the following questions:

  1. How do school administrators and teachers integrate predictive governance tools into their actions, respectively, how have their actions and thus, if applicable, the social relations in the school changed as a result of the tool?
  2. To what extent does the “flagging” of students change the way they are talked about and assessed? How are early warning indicator systems and previous (professional) knowledge being put in relation to each other?
  3. How do school administrators and teachers perceive the effects of “flagging” for the students themselves, for example in terms of the production of data biographies and social inequality?

In addition to our own empirical research, we are also interested in the question of the degree to which predictive control has become a core phenomenon of digital society beyond the education sector. In this regard, we cooperate in particular with the professorship of Tobias Scheytt at HSU. In February 2020, we co-hosted a corresponding international workshop with an interdisciplinary focus (PREGOV – Predictive Governance).

Click here to go directly to the PREGOV project page on ResearchGate with further insights.


Letzte Änderung: 5. January 2021