Table 5.

Student characteristics modeled using physiological data.

Student characteristics modeled	Studies that used physiological data	Machine learning algorithms with effective performance	Accuracy of the models
Affective states	Bixler and D'Mello, 2016; Shi et al., 2019; Ashwin and Guddeti, 2020	Bayesian networks, convolutional neural networks and support vector machine	Ranges from 72 to 95.6%
Emotion	Li and Wang, 2018; Hung et al., 2019; Yang and Qi, 2020; Liu and Ardakani, 2022	Convolutional neural networks and k-nearest neighbor	Ranges from 74.3 to 97%
Engagement	Monkaresi et al., 2017; Booth et al., 2018; Liu et al., 2018; Dubbaka and Gopalan, 2020; El Kerdawy et al., 2020; Mohamad Nezami et al., 2020	Convolutional neural networks, random forest, and naïve bayes	Ranges from 70 to 95%
Motivation	Santos et al., 2020; Aggarwal et al., 2021; Chattopadhyay et al., 2021; Wang et al., 2022	Support vector machine, convolutional neural networks, and logistic regression	Ranges from 85 to 94%