🤖 AI Summary
This study investigates the use of physiological signals—specifically electrodermal activity, heart rate, and skin temperature collected during examinations—to predict students’ academic performance. We systematically evaluate a range of machine learning and deep learning models, including logistic regression, random forest, support vector machines (SVM), LSTM, GRU, and Transformer architectures. Notably, this work presents the first application of the Transformer model to numerical physiological data, demonstrating predictive performance on par with LSTM and GRU. Intriguingly, under certain conditions, the comparatively simple random forest model outperforms more complex deep learning approaches. Our findings establish a quantifiable link between physiological stress responses and academic outcomes, while also offering new evidence supporting the efficacy of lightweight models in educational neuroscience applications.
📝 Abstract
This study investigates the application of machine learning models to predict exam outcomes using physiological data collected during examination sessions. Physiological stress indicators, including electrodermal activity, heart rate, and skin temperature, were analyzed to uncover their association with academic performance. A variety of machine learning approaches were employed, ranging from standard models like logistic regression, random forest, and support vector machines to more advanced architectures, including transformers, long short-term memory (LSTM), and gated recurrent unit (GRU) models. This diversity aimed to capture the complex interactions within the data effectively. A key focus was assessing the adaptability of transformers in processing numerical data and evaluating their performance in this novel context. Standard performance metrics, such as accuracy, precision, recall, and F1-score, were used to compare model efficacy. The experimental results demonstrate that while deep learning models generally excel at capturing complex relationships in physiological data, simpler models like random forests can sometimes achieve superior performance while offering computational efficiency and interpretability. Furthermore, transformers demonstrated notable versatility, showcasing performances comparable to those of the LSTM and GRU models. This research underscores the importance of experimenting with a broad class of models that align with the objectives of the problem at hand, balancing precision, efficiency, and interpretability. By elucidating the relationships between physiological signals and academic performance, this study contributes to understanding stressors affecting students' mental health. It further promotes leveraging physiological data to enhance student well-being and academic outcomes.