Global Patterns in Student Stress and Academic Performance: A Machine Learning Study Using PISA 2022
Abstract
Machine learning was applied to examine whether stress-related factors influence student performance in a consistent way across the world. The main goal of this project is to confirm or reject the existence of a similar global pattern by generalizing the findings that already exist in this field. We focused on various psychological indicators such as anxiety score, test anxiety, math anxiety, math confidence, wellbeing, and sense of belonging, along with several non-psychological factors for context. Machine learning was chosen due to the extremely large size of the PISA 2022 dataset and its ability to capture complex relationships that simpler methods may overlook. The analysis was conducted across six continents by splitting the dataset into six separate case studies. Feature engineering was performed manually for each region, while the same baseline models were trained to ensure a fair comparison. The results show that the negative effect of stress on performance is present and fairly consistent across all continents. Although some error remains, partly because stress is not the only factor shaping academic outcomes, the overall pattern is clear. Africa stood out as an outlier due to lower average educational and wellbeing levels and a higher proportion of missing data, yet even there the negative relationship remained observable.
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Evidence Hub reads this as a preprint, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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