## 3 Materials and Methods
### Data Collection and Preparation  
The case in this study was a 19-year-old college student obtaining a bachelor of science in medical laboratory science. The proclaimed gender of the participant was female with an ethnicity of Caucasian. She was a sophomore in her studies, making class attendance and learning a familiar process. The college student logged physiological and self-report data while attending her class sessions, which were online due to the COVID-19 pandemic, from 1 September 2020 to 3 December 2020. Online instruction was a new route of learning for her.  
Data were obtained through the use of the Empatica E4 wristband device [48] over the course of the Fall 2020 semester. The E4 collected data for activities such as actively paying attention in class, asking questions, collaborative small group discussions, organizing due dates in a personal planner, and emailing teaching assistants and professors. There were also data sessions that included studying and doing homework outside of class time. These activities included online homework assignments, studying with fellow students, and watching online lecture videos for review of class material. Finally, there were everyday tasks performed while collecting data, such as scrolling on social media, cooking, tutoring, grocery shopping and eating dinner. Overall, the data collection process for all sessions was the same. The E4 wristband was turned on promptly at the start of each class or task and kept on throughout the entire duration of the activity. The watch data collection session ended promptly at the end of a session, and a cognitive load survey was completed [40]. The participant's self-reported cognitive load rating after each activity was based on [PERSON] mental effort 1-9 ordinal scale [40], with 1 being very very low mental effort and 9 being very very high mental effort.  
To predict the mental effort based on the physiological parameters, six measures from the Empatica E4 device were recorded and utilized. These measures were acceleration (ACC) on three orthogonal axes X, Y, and Z; electrodermal activity (EDA) (\(\mu\)S); skin temperature (TEMP) (\({}^{\circ}\)C); and heart rate (HR) (beats per minute). These measures were recorded by the E4 wristband while the student was performing different activities (descriptive statistics in Table 1).  
The E4 wristband sampled heart rate at a frequency of 1 Hz, EDA and skin temperature at 4 Hz, and acceleration at 32 Hz. In order to remove the false readings at the beginning of each session as the device stabilized, we removed the initial 40 readings from heart rate, which was sampled at 1 Hz. Likewise, as EDA and temperature were sampled at 4 Hz, and acceleration was sampled at 32 Hz, we removed the corresponding readings from these physiological parameters as well. Thereafter, to combine all the physiological parameters in a consolidated data set, EDA, temperature, heart rate, and acceleration were downsampled to 1 Hz while accounting for the fact that heart rate was calculated as a moving average of the previous 10 s of data. For each activity, these corresponding physiological parameters were combined and arranged chronologically, after which we found that a total of 92.3 h of data over 91 separate activities were recorded; these data were used for further analysis.