arXiv:2606.14960v1 Announce Type: new Abstract: This study investigates the application of machine learning models to predict exam outcomes using physiological data collected during examination sessions. Physiological stress indicators, including electrodermal activity, heart rate, and skin temperature, were analyzed to uncover their association with academic performance. A variety of machine learning approaches were employed, ranging from standard models like logistic regression, random forest, and support vector machines to more advanced architectures, including transformers, long short-term
The increasing availability of wearable sensors and advancements in machine learning techniques are enabling novel applications for physiological data analysis.
This research demonstrates a potential new avenue for personalized educational interventions and stress management, leveraging passive data collection to inform academic support.
The ability to predict academic performance based on real-time physiological indicators suggests a shift towards more proactive and data-driven student support systems.
- · EdTech companies
- · Educational institutions
- · Students experiencing academic stress
- · Wearable device manufacturers
- · Traditional assessment methods
- · One-size-fits-all educational approaches
Machine learning models trained on physiological data can predict exam outcomes with a measurable degree of accuracy.
Educational systems might adopt personalized stress-reduction interventions based on real-time physiological monitoring.
The integration of such monitoring could lead to ethical debates around data privacy and surveillance in educational settings.
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