A wearable dataset for predicting in-class exam performance

Unfortunately, attempts to simulate their impact in the laboratory or elsewhere are less useful than datasets gathered in real-world circumstances. As a result, researchers have access to fewer real-world stress datasets. Even rarer indeed are such datasets used in longitudinal investigations on the same subjects over time.

Real-world situations are also unrestricted environments. Research-grade equipment is frequently inaccessible, and motion artifact contamination is pervasive. These continue to be some of the biggest barriers to automated emotion decoders outside of the research labs in daily life.

To address the above-mentioned gap, Rose Faghih and her former PhD students Md. Rafiul Amin and Dilranjan Wickramasuriya performed an experiment, in which a set of students' physiological data was gathered over the course of three exams. They used a smartwatch-like wearable device and collected multimodal physiological data. The use of the smartwatch-like wearable device was to provide a seamless data collection experience for the students participating in the experiment.

The investigation shows that it is possible to link the variations in the physiological signals to the exam performance. More details about this study can be found in the corresponding publication titled "A Wearable Exam Stress Dataset for Predicting Grades using Physiological Signals."

To enable other researchers, use this dataset for additional investigations, the research team has made the de-identified data publicly available on the PhysioNet platform. A Wearable Exam Stress Dataset for Predicting Cognitive Performance in Real-World Settings is available at: https://physionet.org/content/wearable-exam-stress

Ultimately, the researchers believe it would be extremely beneficial to consider how exam performance and the stress that goes along with it interact. It will allow for a wide range of potential applications with the aim of enhancing personal performance. This may, for instance, assist scientists in developing effective interventions to improve each person's performance and increase productivity within a company. Additionally, the knowledge may be used in online and remote learning contexts to connect with students effectively and improve learning outcomes.