Since the detection of high and low self-discipline based on self-reported measures is problematic due to validity problems, we have proposed an objective method based on physiological EEG data. Using a fine grained EEG spectrum in combination with machine learning, we were able to build a stable, reliable classifier, which reaches an accuracy of almost 76 percent. Moreover, we show that there are systematic differences in the theta and beta frequencies. Predicting low self-discipline and high self-discipline is an important topic in learning (especially e-learning) research as well as in medicine.
Sauter, Daniel; Butz, Lars; and Buettner, Ricardo, "Machine Learning Based Differentiation Between High and Low Self-Discipline Using EEG Data" (2021). ECIS 2021 Research Papers. 104.
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