Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning

In three linesStudy comparing ML models (logistic regression, random forest, SVM, transformers, LSTM, GRU) to predict exam outcomes from physiological signals (electrodermal activity, heart rate, skin temperature). Random forests outperform deep learning models in computational efficiency and interpretability.

Summary generated by Claude — human-verified