Datafication and Educational Governance in India

• In Event: Educational Transformations in a Digital Age

Proposal

Countries are increasingly collecting large amounts of different types of education related data for various purposes such as student assessment, accountability, competition, school inspection, administrative planning, and pedagogical decisions (Ozga & Lingard 2007; Hursh, 2013; Ozga, 2009; Lawn, 2013; Hardy, 2015; Lewis & Hardy 2017; Lewis & Holloway 2018). This ‘datafication’ (Williamson, 2016) of education is being observed across the world irrespective of the history and culture of education systems and levels of economic development (Kamens 2013; Kamens & McNeely 2010). Scholars describe this as part of a move towards neoliberal governance of education (Rizvi & Lingard, 2010). The rise of data enabled by exponential growth in computation power, digitization, internet access, and information systems and tools, is an instrument of this new governance system (Lawn, 2013). Recognizing this phenomenon, scholars are increasingly identifying the unintended consequences of rising datafication (Gorur, 2020; Lewis, Sellar & Lingard; 2016; Hardy 2015; Williamson 2016, 2017; Lewis and Hardy 2017 etc.).
The existing literature on datafication in education is largely concentrated on experiences of countries like United States, United Kingdom and Australia, and therefore its applicability to other regional and national contexts is debatable(Takayama and Lingard, 2019). For example, developing countries like India are actively pursuing datafication in education and other aspects of governance. The datafication experiences in such contexts could be surrounded by issues such as external interventions in building data systems, resource & digital constrained environments, transparency issues, colonial bureaucratic systems etc. These issues can significantly influence forms, processes, and implications of datafication(see Takayama and Lingard, 2019). Datafication can also work differently in these countries due to vastly different education policy priorities such as improving basic literacy skills, increasing access to schools, reducing dropouts, and bringing marginalized populations to schools. These different priorities could change the types of data collected and prioritized. These issues are not explored in the literature, raising questions about relevance of its findings to wider contexts like India and other countries in the global south.
There is a need for greater regional and national diversity in this literature to demonstrate that datafication can take different forms and assume different meanings (Takayama and Lingard, 2019). Restricting focus on certain contexts also restricts posing of different set of questions that are perhaps more relevant to other national and regional contexts (Takayama and Lingard, 2019). Most importantly, as often seen in other north centered knowledge production, neglecting global south contexts could lead to a global benchmarking of datafication dynamics that could ultimately make southern scenarios appear abnormal or exceptional.
I aim to address the above highlighted gap in the literature by discussing the datafication model adopted by India. India is witnessing some of the largest datafication initiatives in the developing world not just in education (e.g. Unified District Information System for Education-UDISE, National Achievement Survey-NAS etc.) but also other areas of governance (e.g. Aadhar ID). In this paper, I describe how India has adopted western approaches to datafication, but its model has shaped differently due to its developmental and capacity context. This model has not supported outcome and competition driven school accountability system. Here global data strategies are being applied to improve state accountability and prepare grounds for school accountability. This could have different implications compared to insights noted in the literature. It could reveal assumptions in existing literature about nature of emerging data forms, and their role and impact in educational governance. This case could also help to understand different pathways low and middle income countries might take in their journey towards globally inspired educational datafication to suit their local realities and circumstances. These different pathways can represent how context shapes and transforms global ideas and processes into hybrid formats. The findings will be based on analysis of education data structures, policy documents (annual reports, statistical reports, policy frameworks, planning documents etc.), and interviews with officials in charge of developing key data initiatives such as UDISE, NAS and PGI (Performance Grading Index).

Author

• Jainisha Chavda, Michigan State University