## 3 Results
### Objective 2: To Determine Cognitive Load Levels (Low, Medium, and High) in the Participating Groups

Table 4: _Cont._Objective 4: To Apply Supervised Machine Learning Classification Techniques to Identify the Most Representative Classifiers of Cognitive Load  
A discriminant analysis was carried out to determine the behaviours of the groups with respect to the cognitive load indicators. As Figure 3 shows, the behaviour in cognitive load for Group 1 and Group 2, where the students had not previously watched the lab (which contained general health content), was quite similar to each other, but differed from the behaviour of Group 3, where the students had previously viewed the lab (which was about content specific to the degree). Similarly, all the values of Wilks' lambda (a method for selecting variables by discriminant analysis steps that selects the variables to be added into the equation based on how much they contribute to decreasing Wilks' lambda--at each step, the variable that minimises the overall Wilks' Lambda is added) for the cognitive load indicators was significant except for the average GSR and SCR count indicators (see Table 5). The KNN algorithm was also applied, giving the most representative cognitive load indicators as fixation point X, fixation point Y, and mean duration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cognitive Load** & **Lambda** & **F** & **df1** & **df2** & **pLambda** \\
**Indicators** & **Wilks** & & & & & \\ \hline Mean duration & 0.36 & 40.10 & 2 & 45 & \textless{}0.001 * \\ Fixation point X & 0.38 & 36.12 & 2 & 45 & \textless{}0.001 * \\ Fixation point Y & 0.39 & 35.28 & 2 & 45 & \textless{}0.001 * \\ Average pupil diameter & 0.20 & 92.56 & 2 & 45 & \textless{}0.001 * \\ Saccade direction & 0.35 & 41.44 & 2 & 45 & \textless{}0.001 * \\ Average velocity & 0.71 & 9.04 & 2 & 45 & \textless{}0.001 * \\ Peak velocity & 0.62 & 13.91 & 2 & 45 & \textless{}0.001 * \\ Saccade amplitude & 0.42 & 31.26 & 2 & 45 & \textless{}0.001 * \\ Average GSR & 0.93 & 1.79 & 2 & 45 & 0.18 \\ SCR count & 0.94 & 1.43 & 2 & 45 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test of equality of group means. Figure 3: Canonical discriminant functions with respect to the group type variable. Figure 4 shows the representation of the focal points with respect to the fixation point Y classifier and X classifier and the distribution of subjects with respect to the other indicators of cognitive load. Figure 5 shows the representation with respect to the fixation point Y classifier and Figure 6 with respect to the mean duration classifier. The numbers in the focal graphs indicate the participant. Thus, it is possible to know the position of each participant with respect to the classifiers and the focal points. This fact is important for being able to design specific tasks for each of them. Figure 4: Analysis of the focal points with respect to the fixation point X classifier. Figure 5: Analysis of the focal points with respect to the fixation point Y classifier. Objective 5: Apply Unsupervised Machine Learning Techniques to Determine Possible Groupings Without a Prior Assignment Variable with Respect to Cognitive Load Indicators and Learning Outcomes  
Three clusters were found for the indicators of cognitive load and for the results in the learning check test (see Table 6). ANOVA was also performed to test whether there were significant differences between the clusters in the cognitive load indicators and in the test results (see Table 7). Significant differences were found in all cognitive load indicators except average GSR and SCR count. No significant differences were found in the outcome test.