Why East Asian students perform better in mathematics than their peers: An investigation using a machine learning approach

Hanol Lee and Jong-Wha Lee

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Using a machine learning approach, we attempt to identify the school-, student-, and country-related factors that predict East Asian students’ higher PISA mathematics scores compared to their international peers. We identify student- and school-related factors, such as metacognition–assess credibility, mathematics learning time, early childhood education and care, grade repetition, school type and size, class size, and student behavior hindering learning, as important predictors of the higher average mathematics scores of East Asian students. Moreover, country-level factors, such as the proportion of youth not in education, training, or employment and the number of R&D researchers, are also found to have high predicting power. The results also highlight the nonlinear and complex relationships between educational inputs and outcomes.

Keywords: education; East Asia; machine learning; mathematics test score; PISA (search for similar items in EconPapers)
JEL-codes: C53 C55 I21 J24 O1 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-edu, nep-isf, nep-sea and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2021-66

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