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(Methods Note)

Descriptive Learning Trajectories and Policy Simulations Using MICS6 Data

Authors

Image of Michelle Kaffenberger

Michelle Kaffenberger

RISE Directorate

Blavatnik School of Government, University of Oxford

Image of Jason Silberstein

Jason Silberstein

RISE Directorate

Blavatnik School of Government, University of Oxford

In recent years, scholars associated with the RISE Programme have analysed learning trajectories using a variety of global datasets to shed light on the global learning crisis and diagnose what might help address it (Crouch, Kaffenberger, and Savage, 2021). For those who may want to build and analyse learning trajectories, this note acts as a methodological guide for doing so using an important new dataset on foundational learning, the Multiple Indicator Cluster Surveys Round 6 (MICS6). We have applied the methods described in this note and, in partnership with the Global Education Monitoring Report (GEMR), developed a tool to showcase the results. The resulting “Learning Trajectories” webpage serves as an interactive introduction to learning trajectories and related policy simulations, and features a flexible data explorer for those who want to conveniently build, analyse, and apply learning trajectories and policy simulations to their own work and context.

Citation:

Kaffenberger, M. and Silberstein, J. 2022. Descriptive Learning Trajectories and Policy Simulations Using MICS6 Data. Research on Improving Systems of Education. https://doi.org/10.35489/BSG-RISE-Misc_2022/05