Table 2.
Automatic modeling of student characteristics in learning studies using physiological data.
| Studies | Number of students | Learning environments | Collection devices/ methods | Signal types | Student characteristic modeled | Machine learning models | Models accuracy |
|---|---|---|---|---|---|---|---|
| Bixler and D'Mello (2016) | 178 undergraduates | Research texts on research methods presented on computer screens | Tobii TX 300 and Tobii T60 eye tracker | Fixations and saccades | Affective states | Bayesian network | 72% |
| Shi et al. (2019) | 82 students | MOOC platform | Logitech C920 webcam | Video clips of facial expression | Affective states | Convolutional neural networks and support vector machine | 93.8% |
| Ashwin and Guddeti (2020) | 50 students | Classroom environment | Camera | Video clips | Affective states | Convolutional neural networks | 95.6% |
| Hung et al. (2019) | 4 university students | Students learning in a class | Camera | Video records | Emotion | Convolutional neural networks | 84.6% |
| Li and Wang (2018) | 10 students | Intelligent education system | Camera | Video clips of facial expressions (blink frequency) | Emotion | Convolutional neural networks | Not reported |
| Liu and Ardakani (2022) | 15 students | Affective learning system | EMOTIV EPOC and EEG headset | Brain waves pattern | Emotion | K-nearest neighbors | 74.3% |
| Yang and Qi (2020) | 70 students | Not reported | Camera | Pictures of students' facial expressions | Emotion | Convolutional neural networks | 97% |
| Booth et al. (2018) | 10 students | Interactive computer tasks | EPOC and EEG headset | Brainwave data | Engagement | Random forest | 62.5% |
| Dubbaka and Gopalan (2020) | 5 adults | MOOC | Logitech C920 webcam | Video clips of facial expressions | Engagement | Convolutional neural networks | 95% |
| El Kerdawy et al. (2020) | 109 university students | Psychology continuous performance tasks | Camera and EEG headset | Video clips of facial expressions and brainwaves data | Engagement | •Random forest (for EEG data) •Convolutional neural networks (for facial data) | 86 and 82% |
| Liu et al. (2018) | 8 students | Intelligent class environment | Overhead camera in a wide classroom | Visual focus of attention (VFOA) and head pose estimation | Engagement | Hybrid multilayered random forest | 70% |
| Mohamad Nezami et al. (2020) | 20 high school students | Virtual world learning environment named Omosa | Camera | Video clips of facial expressions | Engagement | Convolutional neural networks | 72.4% |
| Monkaresi et al. (2017) | 22 students | Students writing essays using google document | Microsoft Kinect Sensor | Videos of their faces and upper bodies (used for estimation of facial expression and heart rate) | Engagement | Naïve Bayes | AUC 0.76% |
| Aggarwal et al. (2021) | 12 undergraduates | MOOC | EEG headset | Brainwave data | Motivation | Support vector machine | 94% |
| Chattopadhyay et al. (2021) | 30 students | Game environment | EEG known as brainmarker | Brainwave data | Motivation | Convolutional neural networks | 89% |
| Santos et al. (2020) | 45 high school students | Students performing experiments in physics lab | Camera | Video clips of facial expressions | Motivation | Convolutional neural networks | 85% |
| Wang et al. (2022) | 25 university students | OGAMA software | Tobii X120 tracker and EEG headset | Fixation, pupil size, saccades, and Brainwave data | Motivation | Logistic regression | 92.8% |