## 2 Review of Literature  
### The Use of Wearables in Education  
As noted, researchers have investigated the use of wearables in various ways within the educational domain or wearable data in relation to educationally relevant variables. In addition to the themes already discussed, researchers have also examined critical issues such as the feasibility and usability of wearables in educational environments [12, 13], how wearables influence teaching practices [14], and detecting attention or interest [15, 16]. However, work involving wearables in educational settings has often encountered challenges. Two prominent challenges are linking the data from the wearable to the learning process and finding an effective way to analyze the data.  
The question of what is being measured by the wearable in relation to education is particularly notable and deserves closer examination. First, it is critical to define what physiological outcomes can be measured through wearables and may be of interest to educational researchers. Wearables such as the Empatica E4 have been used to measure electrodermal activity, skin temperature, and heart rate [11]. Some have linked wearable data to engagement [17, 18, 19], while others have used the data as indexes or measures of mental effort, mental workload, or cognitive load (for the purposes of this paper, we use these terms synonymously, referring to the load placed on the working memory during a task). Based on existing research, we believe that mental effort or mental workload is an appropriate correlate [1] to physiological activity, because mental workload demands can cause an unconscious, automatic response by the autonomic nervous system [20]. In addition, there is a plethora of research around using physiological measures to examine mental workload or mental effort (for reviews, see [21, 22, 23]).  
The second prominent challenge researchers face is how to best analyze the data from wearables in educational contexts. Some researchers have analyzed physiological variables individually, such as heart rate, electrodermal activity, or skin temperature [18, 24, 25]. However, a recent systematic review revealed that there was no single physiological measure that captured all aspects of mental workload measurement [21]. This could explain why some studies that have examined individual variables have found reasonably accurate results, whereas studies that have taken a multimodal data analysis approach have found more promising results. For instance, one study found that heart rate could predict mental workload changes 62.5% of the time, while galvanic skin response showed 75% accuracy [26]. Meanwhile, [11] used a multimodal approach and found that using a random forest classifier resulted in strong classification accuracy when classifying students as being engaged in activities that required different levels of mental workload (area under the receiver operator characteristic (ROC) curve (AUC) = 0.93-0.99, F1 = 0.85-0.94). While unimodal or multimodal data analysis is a concern, the analysis techniques that are best suited for this type of work have also been explored, ranging from descriptive [18] through machine-learning methods [11, 27]. At the present time there is not enough evidence to say which machine-learning algorithm works best for predicting mental workload. That is one gap in the literature that this study addresses.  
While a basis for connecting physiological measures to mental workload is important, in educational realms it is also important to understand how these measures can be tied back to theoretical foundations about how people learn. In the next section, we explore cognitive load theory (CLT), a widely used theory for designing instruction.