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.

翻译：本研究探讨了应用机器学习模型，利用考试期间收集的生理数据预测考试成绩。我们分析了包括皮肤电活动、心率和皮肤温度在内的生理压力指标，以揭示其与学业表现的关联。研究采用了多种机器学习方法，涵盖逻辑回归、随机森林、支持向量机等标准模型，以及更先进的架构，如Transformer、长短期记忆网络（LSTM）和门控循环单元（GRU）模型。这种多样性旨在有效捕捉数据中的复杂交互关系。一个关键焦点是评估Transformer在处理数值数据方面的适应性，并在此新颖背景下评估其性能。我们使用准确率、精确率、召回率和F1分数等标准性能指标来比较模型效能。实验结果表明，尽管深度学习模型通常擅长捕捉生理数据中的复杂关系，但像随机森林这样的简单模型有时能在提供计算效率和可解释性的同时实现更优性能。此外，Transformer展现了显著的通用性，其性能与LSTM和GRU模型相当。本研究强调了尝试与问题目标相一致的各种模型的重要性，以平衡精度、效率和可解释性。通过阐明生理信号与学业表现之间的关系，本研究有助于理解影响学生心理健康的压力因素，并进一步推动利用生理数据提升学生福祉和学业成果。