Key result
Stress measurement based on mouse and keystroke dynamics was consistent with measurement based on question duration, with a neural network achieving the best classification performance.
Observational (n=190)
Mouse and keystroke dynamics can be used as an unobtrusive method to classify stress levels in e-learning environments.
May support passive stress monitoring in digital learning; leaves open outcome validation before any practice adoption.
This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real-world individual’s affective states. It is also important to ensure that the measurement can be applied regardless of the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes in duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
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Lim et al. (2019) conducted an observational in Stress (n=190). Mouse and keystroke dynamics vs. Changes in duration spent between two consecutive questions was evaluated on Stress classification consistency. Stress measurement based on mouse and keystroke dynamics was consistent with measurement based on question duration, with a neural network achieving the best classification performance.
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