## 1 Introduction
### Cognitive Load Definition and Measurement
However, this remains a challenge, as the same task may need to be presented in different ways for each type of learner, and learners may experience different cognitive loads for the same task [19]. Research is currently ongoing about the cognitive load experienced in virtual environments that apply simulation tasks [19]. These studies show that cognitive load is a useful indicator for analysing learning processes in simulated environments. They also differentiate between intrinsic cognitive load, task-specific germane cognitive load, and extraneous cognitive load. If the total cognitive load (the sum of the three types) exceeds the capacity of working memory, performance may suffer [20]. Another classification of cognitive load differentiates between primary cognitive load (referring to domain-specific task performance) and secondary cognitive load (referring to metacognitive performance that supports primary performance). If the secondary cognitive load is activated, performance may be better but the total cognitive load is increased, and so the relationship between cognitive load and performance may be positive or negative depending on the type of task, the type of learner, the task design, and the context of task performance [21]. Research about performance analysis in simulated learning environments has shown psychophysiological indicators of cognitive load to be the most reliable. These include pupil dilation (an involuntary response reflecting noradrenergic activity of the locus coeruleus that regulates arousal, cognitive activity, and emotion), which can indicate the cognitive load related to the emotion that the task produces in the learner [22; 23]. However, this measure may be affected by factors other than an individual's processing, such as the type of lighting. Other indicators of cognitive load include the duration and total time of fixation [22], the number of saccades, and the amplitude of the saccades [24; 25]. These are all static indicators, but dynamic indicators should also be considered. Dynamic indicators refer to the route that each learner takes in the task resolution process. The route refers to the steps followed in order of priority and the duration of each step in the spatial coordinate space during execution. An analysis of each learner's tracking pattern on the multimedia interface used in the simulation environments can give information about how this information has been retained in working memory and how it has been processed in LTM. The type of interface can increase or decrease a task's cognitive load, depending on the individual learner [26]. In summary, physiological measurements during task performance can provide information on different indicators such as pupil diameter [27] or the galvanic skin response (GSR), which measures skin conductance by recording the sweat secreted from the sweat glands [28]. This can be divided into tonic components, giving information on the level of skin conductance, and phasic components, providing the skin conductance response (SCR), a rapidly changing signal that takes one to five seconds to reflect the response to a stimulus [29]. These two measures, GSR and SCR, are considered reliable indicators for measuring cognitive load levels [27; 29], although they can be contaminated by other external factors. As noted above, pupillary activity can be subject to illumination-related factors [30]. Similarly, skin conductance indicators may be affected by the user's physical activity (e.g., GSR-equipped hand movements) [29]. Therefore, to prevent external or internal factors from affecting the physiological measurement of cognitive load, these external variables should be controlled as much as possible [31]. Moreover, current lines of research are aimed at using several indicators to measure cognitive load, understanding this as a multimodal approach [31; 32].