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Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice


The field of learning analytics is being discussed in many circles as an emerging concept in education. In many districts and states, the core philosophy behind learning analytics is not entirely new; for more than a decade, discussions of data-driven decisionmaking and the use of data to drive instruction have been common. Still, the U.S. education system has not yet come close to reaching the potential of learning analytics.

The learning analytics initiatives described in this paper are helping states and districts move from being data collectors to being data analyzers, able to use the vast amount of information being collected in a secure, practical, customized, and predictive system. Ultimately, many of the examples provide a glimpse into how districts are preparing to take advantage of learning analytics to meet the needs of each student. This transition is not just about implementing new or better data or assessment systems, or even improving the analysis of data. Education systems must consider capacity in infrastructure and human capital, data use culture in schools and communities, and policies that enable the meaningful use of data to effectively apply and use learning analytics.