Exam Stress Visualization

Project Overview

In this project, we explore the relationship between academic stress and physiological responses by analyzing data from wearable sensors collected during midterm and final exams. Our goal is to uncover how cognitive pressure manifests in real time through measurable signals such as heart rate (HR) and electrodermal activity (EDA). This work helps illustrate the invisible toll of academic pressure and offers insight into how stress varies by individual and context.

Going into this, we expect there to be an optimum level of stress for exam performance. Obviously, if you're too stressed, you can't focus properly on the exam and you'll do worse, but we also expect that if you go in not stressed at all, your brain won't be able to focus as well and won't see the proper 'threat' of the exam to trigger the correct signals to your body. Because of this, we expect that there will be a level of stress inbetween that you can train your body to perform at, to increase your exam performance.

Interactive Visualizations

Heart Rate Over Time

This line chart shows the average heart rate of students during exams. Users can filter by gender group to observe trends across male and female students. The time axis spans a 3-hour exam window, and hovering reveals minute-level HR details. Design choice: A line chart was chosen for its effectiveness in showing temporal trends, especially for identifying patterns such as pre-exam spikes or post-midpoint plateaus.

Group:

Signal Strength vs Exam Score

This scatterplot correlates students' average physiological signal levels (e.g., EDA or HR) with their performance on exams. Users can switch between midterm and final data. Design choice: We used scatterplots to investigate possible trends between stress indicators and outcomes, encouraging exploration of whether heightened physiological response relates to higher or lower academic performance.

Exam Scores are in percentage. Both midterms were out of 100, but the final was 200 points and is normalized

What We Learned

Heart rate typically spikes near the end of exams, likely reflecting time pressure or final effort.
Female students demonstrated more variability in EDA during finals, suggesting different stress coping mechanisms.
Physiological responses varied more by individual than by exam type, emphasizing personal stress thresholds.
But the main takeaway from all this is that there's very little correlation between stress levels and exam results, meaning that unfortunately there is no easy way to get ahead on your exams. You're stuck studying hard if you want to do well

Dataset & Methodology

We used the publicly available Wearable Exam Stress Dataset from PhysioNet, which includes continuous recordings of heart rate (HR) and electrodermal activity (EDA) collected during real university exams.

Data processing steps included:

Resampling data for consistent time intervals
Converting timestamps to relative exam time
Grouping by student and exam type
Calculating averages and ranges for visualization

All preprocessing was done in Python using Pandas and NumPy. The visualizations were developed with D3.js to support interactivity and dynamic exploration.

Limitations & Considerations

The dataset lacks baseline readings outside of exams, making it difficult to compare to “normal” physiological states.
Movement artifacts or environmental factors (e.g., room temperature) may influence signal accuracy.
Sample size and demographic variety are limited, so generalizations should be made cautiously.

About the Team

We are a group of students passionate about data storytelling. This project was built collaboratively as part of our final visualization course assignment.