Predicting academic emotion based on brainwaves signals and mouse click behavior

Academic emotions such as confidence, excitement, frustration and interest may be predicted based on brainwaves signals. It is shown that the prediction rate can be improved further when the data from brainwaves signals are complemented by data based on mouse click behavior. Twenty-five (25) undergraduate students were asked to use a math tutoring software while an EEG sensor was attached to their head to capture their brainwaves signals throughout the learning session. At the same time, mouse-click features such as the number of clicks, the duration of each click and the distance traveled by the mouse were automatically captured. Using a Multi-Layered Perceptron classifier, classification using brainwaves data alone had accuracy rates of 54 to 88%. Prediction rates based purely on mouse features had accuracy rates of only 32 to 48%. When the two input modalities are combined, accuracy rates increased to up to 92%. Furthermore, the experiments confirmed that the predication accuracy rate increases as the number of feature values that deviate significantly from the mean increases. In particular, the prediction rates exceed 80% when at least 33% of the features have values that deviate from the mean by more than 1 standard deviation