# Cloud-Based Collaborative 3D Modeling to Train Engineers for the Industry 4.0
## 3 Results  
[PERSON]'s alpha statistic was calculated to assess the reliability of the scale and a high value was obtained (\(\alpha\) = 0.838). Likewise, the reliability of F1 (\(\alpha\) = 0.829), F2 (\(\alpha\) = 0.782) and F3 (\(\alpha\) = 0.714) have been calculated. Consequently, descriptive analyses of the mean, median and standard deviation were carried out (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Item** & **Mean** & **Median** & **Std. Dev.** &
\begin{tabular}{c} **Factor Reliability** \\ **(Cronbach alpha)** \\ \end{tabular} \\ \hline \multirow{4}{*}{F1} & V1 & 3.85 & 4.00 & 0.96 & \multirow{4}{*}{0.829} \\  & V2 & 4.46 & 5.00 & 0.64 & \\ \cline{1-1}  & V3 & 4.62 & 5.00 & 0.49 & \\ \cline{1-1}  & V4 & 3.46 & 4.00 & 1.01 & \\ \cline{1-1}  & V5 & 4.69 & 5.00 & 0.61 & \\ \hline \multirow{4}{*}{F2} & V6 & 3.46 & 4.00 & 1.22 & \multirow{4}{*}{0.782} \\  & V7 & 2.62 & 2.00 & 1.34 & \\ \cline{1-1}  & V8 & 2.15 & 2.00 & 1.41 & \\ \cline{1-1}  & V9 & 2.00 & 2.00 & 1.04 & \\ \cline{1-1}  & V10 & 2.00 & 2.00 & 1.05 & \\ \hline \multirow{4}{*}{F3} & V11 & 4.54 & 5.00 & 0.50 & \multirow{4}{*}{0.714} \\  & V12 & 3.92 & 4.00 & 1.15 & \\ \cline{1-1}  & V13 & 3.69 & 4.00 & 1.14 & \\ \cline{1-1}  & V14 & 4.5 & 5.00 & 0.75 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation results. Figure 4: (a) 3D models in Sketchfab; (b) Augmented reality; (c) 3D printed model. Figure 3: A360 interface with data management options, calendar and conversations. Regarding F1 (collaboration and design features), the students scored all the items close to two values: 3.60 (coordination, collaboration and communication features) and 4.50 (geometry and design usability and its viability to reduce face-to-face meetings). In terms of PLM features, students scored values close to 2.00 for all the PLM tools. However, they assigned a 3.46 to the effectiveness of the roles to collaborate. Finally, students highly scored the third factor (collaborative engineering); they scored values close to 3.8 (for the 3D public viewer and the app as a PDLM) and 4.5 (for utility for understanding a collaborative engineering environment and the need to add permissions). Moreover, Figure 5 represents the distribution of UPDLM and grouping variables. The range of values varies among one and five points. Students scored F1 and F3 variables with high values (mostly from 3 to 5 points) and F2 with low values (mostly from 1 to 3 points). In a few cases, groups provided data points that deviate significantly from the mean values in a rather consistent manner (See the Table S1 in Supplementary Materials). For example, students normally rated the variable 8 with a low value (1, 2 or 3). However, two groups perceived this variable with a value of 5. Both groups that rated V8 with 5 points, also gave a high value (4 points) to the grouping variable of PLM previous knowledge. Meanwhile, the students that rated V8 with a low value indicated their PLM previous knowledge with a similar value. Subsequently, UPDLM mean values were represented in line graphs (see Figure 6) according to the score that the students attributed to the grouping variables: individual practices utility, PLM knowledge, and collaborative engineering perception. A color code has been used to easily differentiate the membership factor (F1 = green, F2 = blue and F3 = red). Figure 5: Box plot of UPDLM and grouping variables. On the one hand, the grouping variable PLM knowledge exhibits the following trends. The variables belonging to F1 oscillate their scores by an average of 1.2 points, F2 variables by 2.04 points and F3 variables by 1.10 points. Also, there is an inverse trend from the 3-point valuation of the grouping variable for most variables (except for three variables of F2 and one of the F1). On the other hand, in the individual practices utility group, the results indicate a direct relationship in all the variables except one (V12).