## 3 Results
### Objective 2: To Determine Cognitive Load Levels (Low, Medium, and High) in the Participating Groups  
Descriptive statistics, maximum and minimum values, and percentiles were calculated for each of the characteristics. The 25 th percentile indicates a low value, the 50 th percentile an intermediate value, and the 75 th percentile a high value, see Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Indicators of**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multicolumn{3}{c}{**Percentiles**} \\ \cline{3-4}
**Cognitive Load** & & & & & **25** & **50** & **75** \\ \hline
**Group 1** & & & & & & \\ Mean duration & 140.72 & 34.77 & 105.33 & 224.75 & 118.63 & 140.72 & 174.36 \\ Fixation point X & 994.84 & 288.42 & 750.66 & 1924.86 & 798.22 & 994.84 & 1227.70 \\ Fixation point Y & 866.17 & 195.53 & 635.68 & 1247.70 & 693.81 & 866.17 & 1048.09 \\ Average pupil diameter & 5311.03 & 1286.28 & 3715.67 & 791.97 & 4338.36 & 5311.03 & 6558.44 \\ Saccade direction & 278,133.97 & 70,405.30 & 130,426.22 & 431,082.91 & 238,321.58 & 278,133.97 & 329,400.50 \\ Average velocity & 233,191.56 & 250,444.66 & 152,868.39 & 1,260,057.37 & 187,812.06 & 233,191.56 & 339,436.29 \\ Peak velocity & 336,697.42 & 260,723.55 & 230,621.84 & 1,398,529.00 & 261,396.24 & 336,697.42 & 493,273.13 \\ Saccade amplitude & 6173.42 & 2359.82 & 2020.85 & 10,407.48 & 5115.04 & 6173.42 & 8437.59 \\ Average GSR & 1.47 & 1.38 & 0.01 & 7.44 & 1.47 & 1.47 & 1.47 \\ SCR Count & 10.18 & 5.76 & 0.00 & 34.00 & 10.13 & 10.18 & 10.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics and percentiles for cognitive load indicators. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Research Objectives**} & \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Test Instrument**} \\ \hline Objective 3 (continued) To apply principal component analysis to determine the groupings of the cognitive load indicators. & Principal component analysis & SPSS v. 28 [65] \\ \hline Objective 4. To apply supervised machine learning techniques for classification in order to determine the most representative classifiers of cognitive load. & Discriminant analysis & SPSS v. 28 [65] \\ \hline Objective 5. To apply unsupervised machine learning techniques to find possible groupings without a prior assignment variable & \(k\)-means & SPSS v. 28 [65] \\ with respect to cognitive load indicators and learning outcomes. & & \\ \hline Objective 6. To determine whether there are significant differences in the indicators of cognitive load and learning outcomes & Two-factor fixed-effect & SPSS v. 28 [65] \\ depending on the type of task and the way it is presented & MANOVA & \\ (visual–auditory vs. visual) in the participating groups. & & Structural equation \\ \hline Objective 7. To propose an analysis model for the indicators of cognitive load. & modelling & AMOS v. 29 [66] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Objective 3: To Apply Supervised Machine Learning Prediction Techniques to Determine How the Variables of Presentation Type 1 (Not Having Previously Watched the Lab vs. Having Done So) and Presentation Type 2 (Visual-Auditory vs. Visual) from the Virtual Laboratories Predict the Indicators of Cognitive Load and Learning Results  
The presentation type 1 variable (not having previously watched the lab vs. having done so) predicted 76.3% (R\({}^{2}\) = 0.763) of the variance with respect to the cognitive load indicators (see Supplementary Materials, Table S1 for the results of the linear regression analysis). The values of tolerance--the proportion of variance not explained by the rest of the variables (1-R\({}^{2}\)), where the higher the Tolerance, the more independent the variable in question--were all less than 1.