Explaining and Resisting the Educational Algorithmization of Societal Transformation in Educational Systems Worldwide

• In Event: Activism and Social Change

Proposal

While the significance of evidence-based research transfer is not an entirely new one in the context of social transformation (BMBF & DIPF, 2007; Wittmayer & Hölscher, 2017), major transformations such as migration, climate change, global inequalities, and digitalization present major social challenges (Loorbach, Frantzeskaki, & Avelino, 2017), which could be resolved or at least informed by empirical research. At the same time, the topic of research transfer to education policy and practice is increasingly an essential requirement for research funding (e.g., BMBF, 2019), and has become a data-driven process in educational systems worldwide. In this paper, we examine the theoretical approaches to social transformation and evidence-based education research transfer framed by the scientization of education and algorithmization of educational policy. We also examine the important contributions that resistance brings to these relationships and the ways that culture contextualizes these processes in educational systems worldwide.
We begin from the premise that science has a special role in the study of social transformation processes. On the one hand, it can describe and analyze current transformative processes, but on the other hand, it can also contribute to concrete solutions by taking a more explicit stand (Wittmayer & Hölscher, 2017; Loorbach, Frantzeskaki & Avelino, 2017). In any case, science has a special function, since it is not only responsible for generating evidence-based knowledge - knowledge that is particularly important in unclear, dynamic and open-ended transformation processes (Kollmorgen, Merkel, & Wagener, 2015) - but also has a social responsibility to solve urgent societal problems. This brings science into a constant process of self-reflection and consideration of its own positioning. Likewise, questions of knowledge transfer and the accessibility of science come into focus. Knowledge transfer aims not only to transfer knowledge unidirectionally, but also to involve different target groups in the process, while the accessibility of science touches on aspects of open science and participation in the knowledge society.

Scientization of Education and Social Change

Late modern societies are societies of scientization. Since the end of WWII, and closely connected to the expansion of education systems worldwide, the legitimizing authority of science became a major societal force and global cultural-cognitive institution (Drori et al. 2002; Drori & Meyer 2006; Drori & Meyer 2009). As a result, the legitimizing authority of science deeply affected the formal structures of nation-states and educational organizations and also served as a blueprint for social transformation. The global expansion of education in the 20th century led to a science-oriented mass education that, when it encountered simultaneously occurring digitalization and technologization processes, served as an ubiquitous dissolution of boundaries with massive effects for collective bodies as well as for individuals.
Scientization is a constitutive global cultural-cognitive pattern which is apparent in the diffusion of science-like legitimizing scripts and rationalizing logic to all spheres of life often facilitated by or through formal education systems. Formal and especially mass education produces citizens who create, transmit, and amalgamate knowledge, creating and repackaging it as a product, traded similarly to tangible goods in the past. Technology has developed to facilitate the flow and manipulation of information (Voogt & Roblin, 2012), with people and machines cooperating in a “cyborg dialectic” (Amos, Wiseman, & Rohstock, 2014). To structure the analyses of this discourse, the phenomenon of scientization of education is defined by four key categories identified through discussion of educational discourse from a variety of sources and previous work in this area (see Amos, Wiseman, & Rohstock, 2014; Wiseman et al, 2016). The four categories are: (1) commodification of knowledge, (2) education as a panacea, (3) cyborg dialectic, and (4) quantification of education (Wiseman et al, 2016).
Policymakers place great faith in the power of data to alter practice (Wiseman & Davidson, 2018). Nonetheless, the fate of policymakers’ attempts to use data to drive educational change depend in large measure on the data-related practices they advocate, mandate, or fund (Wiseman & Davidson, 2018). There is evidence of an increasingly taken-for-granted application of large-scale educational assessment data to educational policy agendas, reforms, and practice (Haertel, 2016; Smith & Baker, 2001; Wiseman, 2010). This phenomenon is often explained as evidence- or data-based decision-making and has been a staple of educational policy and reform for decades. However, there is a distinct difference between data-driven and data-based decision-making. Data-driven decision-making includes policies that begin with and are therefore driven by quantitative evidence, whereas data-based decision-making is spurred by a human interaction with the context and other evidence to adopt or create an appropriate response.

Transformative Processes & Rationales for Resistance

Given that schools are the place where almost all children and young people can be universally reached, educational policies often focus on schools’ capacities for change (Stoll, 2009). However, it is consistently unclear to what extent educational policy measurably influences sustainable practice. There remains a gap in the research literature which does not explicitly identify the de facto relationship that data has with policy, reform, and practice. The evidence is also unclear about the distinction between data-based decision-making, which relies on human interaction with context & evidence to adopt appropriate response (contextualized decision-making), and data-driven decision-making, which begins with and is driven by quantitative evidence without contextual or experiential input (centralized decision-making). Finally, previous research has not clearly identified ways that educational agency and control has shifted from campus and classroom leaders to data and assessment systems, especially in low-income, ethnic/linguistic minority communities.
Resistance to the algorithmization of educational change and its effects on social transformation involves resistance to (a) vicious cycles of educational assessment, (b) rhythmic application of data to educational agendas and issues, (c) indulgence in data (i.e., uncontrolled analysis and interpretation), (d) virtual realities of educational and social needs through the macro-ization of data, (e) value-free additions to assessment without accompanying beneficial impact for target communities, and (f) zombie policymaking, which is the ability of someone or something else to control the educational policies and applications that lead to or influence social transformations.

Authors

• Alexander Wiseman, Texas Tech University

• Annika Wilmers, DIPF - Leibniz Institute for Research and Information in Education

• Lisa Damaschke-Deitrick, University of Tübingen