Abstract
This study presents an extended dataset on educational quality covering 101 countries, from 1970 to 2023. While existing international assessments, such as the Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), offer valuable snapshots of student performance, their limited coverage across countries and years constrains broader analyses. To address this limitation, we harmonized observed test scores across assessments and imputed missing values using both linear interpolation and machine learning (Least Absolute Shrinkage and Selection Operator (LASSO) regression). The dataset included (i) harmonized test scores for 15 year olds, (ii) annual educational quality indicators for the 15–19 age group, and (iii) educational quality indexes for the working-age population (15–64). These measures are provided in machine-readable formats and support empirical research on human capital, economic development, and global education inequalities across economies. Dataset: The datasets supporting the findings of this study are publicly available at https://doi.org/10.5281/zenodo.16778072. Dataset License: Creative Commons Attribution 4.0 International (CC BY 4.0)
| Original language | English |
|---|---|
| Article number | 130 |
| Journal | Data |
| Volume | 10 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2025 Aug |
Bibliographical note
Publisher Copyright:© 2025 by the authors.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
Keywords
- PISA
- TIMSS
- cross-country dataset
- educational quality
- machine learning
- test scores
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Information Systems and Management
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