The paper discusses the role of big data in monitoring education quality and implications at the global, regional and national levels in the light of international efforts to monitor progress in achieving UN Sustainable Development Goal in Education (SDG 4). Data on learning outcomes are central in establishing and monitoring education quality. The proportion of children achieving at least a minimum proficiency in reading and mathematics is a primary indicator of SDG 4. For this indicator to be meaningful across contexts, a shared understanding must be reached on its constituents and the data used to report progress. While large-scale assessments are widely recognized as a primary source for such data, they vary in method and scope, posing major challenges for global monitoring. Consequently approaches to link major cross-national assessments and to harmonise quantitative data across such programs seem promising, despite their limitations in reach and in providing substantive information to inform improvements. In contrast, common described scales provide a reference point for data from a range of different assessments, be they international or national in scope, and including learning data on out-of-school children. The empirically derived scales describe learning progressions in reading and mathematics demonstrated by young learners. Building a bridge between statistical and conceptual approaches, the described scales enable big data from multiple surces to be used, and to be translated into meaningful descriptions of learning and targeted interventions. The scale development program also strongly focuses on capacity building. Observed challenges are articulated
by the example of countries in South Asia that are still struggling to build assessment capacity. For global education monitoring to be effective, ‘big data’ need to be of highquality, internationally consistent, and relevant for the education systems concerned.
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- November 18, 2018 Create Date
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