## 3 Results
### BIM Methodology and the Use of the 3D Tool  
Using the responses to questions Q1, Q2, Q5, Q6, Q8, Q9, and Q10 of Table 2, the students' views regarding the BIM methodology and the use of the 3D tool in the practical part of the course were studied. A factor analysis was performed to reduce the number of variables. The number of factors extracted was three (the following were considered: Guttman-Kaiser rule, percentage of cumulative variability (\(\geq\)80% in total), and [PERSON]'s scree test), with the factors explaining 94.91% of the variance. After varimax rotation, it could be observed that factor DBR1 was associated with variables Q1 and Q2; factor DBR2 was associated with variables Q5, Q6, and Q9; and factor DBR3 was associated with variables Q8 and Q10 (Table 3). The obtained KMO index was 0.742, and Cronbach's alpha was 0.962. The factor loadings and the scree plot are shown in Figure 3.  
Factor DBR1 can be interpreted as the \"level of utility and assimilation of BIM by the student\"; factor DBR2 can be described as \"the interest in BIM and the effort required to learn the tool after the experience\"; and factor DBR3 can reflect the \"complexity and level of learning of the tool.\"  
An AHC analysis was performed using factors DBR1, DBR2, and DBR3 when the students were distributed into six classes. Figure 4 shows the dendrogram, the within-class variance according to the number of classes, and the cluster profile. The full dendrogram displays the progressive clustering of the samples. The dotted line represents the truncation, leading to six groups: Cluster 1 (displayed in blue), cluster 2 (displayed in violet), cluster 3 (displayed in red), cluster 4 (displayed in dark green), cluster 5 (displayed in purple), and cluster 6 (displayed in green). The horizontal axis represents the samples, while the vertical axis represents the dissimilarity coefficients. The samples are identified by two numbers; the first represents the year, and the second represents the order number. The results reflect a within-class variance of 35.00% and a between-class variance of 64.99%. Clusters 1 and 5 had the highest number of students (29.1% and 27.3%, respectively), followed by 3 (16.4%), 6 (10.9%), 2 (9.0%), and 4 (7.3%). Cluster 1 (29% of the samples) contains students with a high perception of the level of BIM utility and their own learning outcomes, defining the tool used for the BIM implementation as intuitive and easy to learn, while Cluster 5 (27% of the samples) groups students with a low perception of the BIM utility level and their own learning outcomes, defining the tool used for the BIM implementation as nonintuitive and complex to learn. Cluster 3 (16.4% of the samples) groups students with a high perception of the BIM utility level and their own learning outcomes who considered the tool used for the BIM implementation as moderately intuitive and complex to learn. When analyzing Cluster 2, it can be observed that 9% of the sample, which defined the tool used for the BIM implementation as not very intuitive and complex to learn, showed a high opinion regarding the usefulness of BIM after implementation. Cluster 4 shows that 7% of students did not consider BIM useful for performing technical projects, considered the tool moderately complex, and evaluated BIM after studying the subject as having moderate utility. Cluster 6 (10.9% of the sample) represents students who consider the tool complex and perceive BIM as unhelpful. The Friedman test of the six clusters reflected a \(p\)-value \(<\) 0.001. Since the computed \(p\)-value was lower than the significance level alpha = 0.05, the null hypothesis H0 should be rejected. The samples did not come from the same population. The multiple comparison post hoc analysis reflected a \(p\)-value \(<\) 0.001 for the qualification variables, indicating the presence of statistically significant differences between the clusters with respect to all the classification variables. Figure 3: Factor analysis: Factor loadings after varimax rotation.