Using Random Forest and Multilevel Modeling to Uncover ICT Predictors of Mathematics in PISA 2022

• In Event: AERA Research in Progress Roundtable Session Four
  In Invited Roundtable: Research-in-Progress RT026 (Saturday)

Abstract

The growing emphasis on digital transformation in education prompts systematic inquiry into the influence of information and communication technology (ICT)-related factors on students’ mathematical literacy in digital learning environments. This study investigates whether student- and school-level ICT factors influence students’ mathematical literacy in digital settings using the 2022 PISA data set encompassing 613,744 students from 81 countries. Random Forest, a machine learning technique, and Multilevel Modeling will be used to analyze the data. Theoretically, this study will clarify how ICT-related factors shape students’ mathematical literacy. Methodologically, it will introduce an innovative analytical method for mathematics education research using large-scale data. Practically, it will inform and enhance pedagogical strategies for technology-enhanced mathematics learning.

Authors

• Linlin Li, McGill University

• Yun Ma, Qingdao University

• Zhengyu Yang, University of Hong Kong

• Yiwen Yan, McGill University

• Yimei Zhang, McGill University

• Yajie Song, McGill University

• Maria Cutumisu, McGill University