Uncovering Key Factors of Student Performance in Math: An Explainable Deep Learning Approach Using TIMSS 2019 Data

In 2019, the TIMSS study offered a closer look at how Moroccan eighth-grade students were doing in mathematics. The data came from a sample of 8390 students; 37% performed well, while the remaining 63% struggled. The goal was to better understand which contextual factors truly influence academic success. The dataset was dense, with over 700 variables drawn from students, teachers, and school questionnaires. To make sense of it, advanced machine learning techniques were applied, including an autoencoder to reduce dimensionality. This process helped narrow things down to 20 key variables that strongly correlated with student performance. These factors covered a range of influences, from teaching strategies and student engagement to teacher training and school-level resources. The insights from the study offer practical guidance for educators and policymakers looking to design targeted, effective interventions. At its core, the study underscores a familiar truth: success in math does not hinge on a single element but on a web of interconnected conditions. Improving outcomes requires a holistic approach, one that supports both learners and the people guiding them.