The following project contains materials for the project associated with the Registered Report to the journal of *Infant and Child Development: prenatal, childhood, adolescence, emerging adulthood* for the Special Issue: Registered Reports with Secondary Developmental Data (Prenatal through Early Adulthood) . **Documents Included on this OSF page** 1. Proof of revised Stage 1 Registered Report submitted to *Infant and Child Development: prenatal, childhood, adolescence, emerging adulthood* February 21, 2022. This version of the Stage 1 was in-principle accepted on Februaary 25, 2022. 2. The associated title page for the revised Stage 1 Registered Report submitted to *Infant and Child Development: prenatal, childhood, adolescence, emerging adulthood* February 21, 2022. **Highlights** 1. Context questionnaires from the TIMSS 2019 grade 8 data will be used to predict student math achievement. 2. To prevent the overuse of secondary data, iterative cross-validated machine learning processes examine the most important predictors of math achievement. 3. The proposed techniques will shed light on the important contextual factors for math achievement. **Abstract** Children’s early math skills are critical for future academic success. To profile the most important predictors of student math achievement, this study applies empirically driven supervised machine learning (ML) techniques to the Trends in Mathematics and Science Study (TIMSS) dataset that is a large-scale, international, nested, secondary dataset. This study seeks to determine what model class (random forest, gradient-boosted trees, multivariate adaptive regression splines, or stacked generalization) is best at reducing model error in predicting student math achievement and how these models differ across 39 countries. By using cross-validated iterative ML techniques, it also aims to establish the student, teacher, and school characteristics that are critical in predicting math achievement among 8th grade students. While the methods in this study do not use inferential statistics to examine math achievement, the predictive modeling techniques utilized may help us shed light on the contextual factors and/or culture that may account for differences in student math achievement as well as how analogous these modeled traits are across countries.