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Understanding Science Achievement Using Large-Scale and Longitudinal Data

Fri, April 10, 1:45 to 3:15pm PDT (1:45 to 3:15pm PDT), JW Marriott Los Angeles L.A. LIVE, Floor: Ground Floor, Gold 4

Session Type: Roundtable Session

Abstract

This roundtable brings together four complementary studies that use large-scale and longitudinal data to uncover the factors shaping science achievement across national and international contexts. Presenters employ advanced analytic methods, including machine learning, causal forests, post-selection inference, and accelerated longitudinal design, to examine predictors ranging from emotional engagement and digital resource use to school climate and urban–rural disparities. Together, these studies reveal nuanced patterns in how social, affective, and contextual variables influence students’ science learning trajectories over time. The discussion will explore implications for addressing inequities, designing supportive learning environments, and leveraging big data to generate insights for science education policy and practice worldwide.

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