Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning
Researchers have explored the use of machine learning to predict exam performance by analyzing physiological signals such as heart rate and electrodermal activity. The study employed a range of models, from traditional logistic regression and random forests to advanced architectures like transformers, LSTMs, and GRUs. While deep learning models showed promise in capturing complex data relationships, simpler models like random forests sometimes offered better efficiency and interpretability. Transformers also demonstrated notable versatility, performing comparably to LSTMs and GRUs in this context. AI