Prediction of exam scores using a multi-sensor approach for wearable exam stress dataset with uniform preprocessing.

Abstract

Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions. The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance. We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features for robust and accurate predictions. The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction. The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions.