## 4 Results
### Machine Learning Classification Approach  
The machine learning approach aims to analyze whether the answers provided by the students are conditioned or not by their particular degree. The comparison is carried out for each two degrees as shown in Figure 6. If the classifier provided an adequate classification on the basis of success indicators (precision, overall accuracy, Kappa index and AUC), there is an effective different response pattern for each of the degrees. Machine learning algorithms are trained using different variables to obtain the best results using the prediction features stated in Table 9, which showed better indicators of success. The most effective parameter to evaluate the prediction capacity of each method is the AUC. If this value is 0.5 or less, the algorithm is making predictions with an accuracy close to randomness. It means that a classification is not possible for the algorithm. Figure 5: Results grouped for each category for all the students (global) and for each bachelor degree. Figure 6: Workflow for the analysis based-on machine learning from the questionnaire response patterns. As is shown in Table 10, based on features applied (Table 9), the AUC is above 0.5 for the comparison between the B.Sc. degrees in electrical engineering and the B.Sc. degree in mechanical engineering. For the comparison between the B.Sc. degree in electronic and automatic engineering with respect to the B.Sc. degree in mechanical engineering. However, for the comparison between the B.Sc. degree in electrical engineering with respect to the B.Sc. degree in electronic and automatic engineering. Therefore, the classifier could not predict successfully (lowest values for the four indicators of Table 9). Please note that the data collection is limited, and very different subjective parameters are being evaluated, so the AUC and Kappa values do not show the high accuracy typical of other applications of machine learning [43; 44]. However, this analysis is not oriented to establish a high accuracy predictive algorithm, but this approach is oriented to demonstrate differences in the questionnaire response patterns for each student. If it is possible to predict the degree, then it is demonstrated that degree conditioning factors to the activity in a non-homogeneous sample with students from different B.Sc. degrees who carried out the same activity at the same time. Different experiments were carried out in order to find the predictor features which provide better Kappa and AUC results. When the questionnaire answers are compared between the B.Sc. degrees in electrical engineering and the B.Sc. degree in mechanical engineering, the best results are obtained with the Bayesian network classifier, achieving an overall accuracy of 80.9% and a Kappa index of 0.530. These results are significant for the quality of the success of the classification between the two groups. Regarding the AUC, for all four tested classifiers the value obtained is higher than 0.5, being the most optimal AUC result (0.768) obtained for the random forest classifier. In the comparison between the B.Sc. degrees in mechanical engineering and the B.Sc. degree in electronic and automatic engineering, the Bayesian network classifier achieved the best results, with an overall accuracy of 84.6%, a Kappa index of 0.581 and an AUC of 0.728. The rest of the classifiers achieve lower performance results, but always higher than the randomness threshold (0 for Kappa, and 0.5 for AUC). While for the two comparisons mentioned above the algorithm test can classify with acceptable accuracy the response patterns (results parameters with a success rate higher than randomness), when the same classifier algorithms are trained and applied between the B.Sc. degree in electrical engineering and the B.Sc. Degree in electronic and automatic engineering, all classifications cannot classify satisfactorily the degree based on the chosen features. More specifically, three of the classifiers show a negative Kappa index, and the highest AUC is 0.44 (lower than the 0.5 threshold).