# Challenge-Based Learning and Scrum as Enablers of 4.0 Technologies in Engineering Education
## 3 Results
### Analysis of the Results Obtained
In Civil Engineering (CivEng), three types of technologies were used, where AI predominated with 54%, Robotics with 32%, and IoT with 14%, where the highest preference for AI was by female students, with 83%, while in the case of men, it was 44%. In the case of Mining Engineering (MinEng), 100% of male and female students used AI as a 4.0 technology to carry out their solution. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Degree** \\ **Program** \\ \end{tabular} } & **Use of Industry 4.0** & **No Use of Industry 4.0** \\  & **Technology** & **Technology** \\  & **\% (Number of Students)** & **\% (Number of Students)** \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} IndEng \\ Male \\ Female \\ Total \\ \end{tabular} } & \multirow{2}{*}{70 (31)} & \multirow{2}{*}{30 (13)} \\  & 60 (9) & 40 (6) \\  & 68 (40) & 32 (19) \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} CivilEng \\ Male \\ Female \\ Total \\ \end{tabular} } & \multirow{2}{*}{80 (16)} & \multirow{2}{*}{20 (4)} \\  & 100 (6) & 0 (0) \\  & 85 (22) & 15 (4) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Use and non-use of industry 4.0 technologies. The above results show a clear trend in each engineering area, evidencing how the preference and use of Industry 4.0 technologies vary according to the engineering specialty. In IndEng, the use of IoT is predominant, which suggests that students perceive this technology as fundamental to optimizing production processes. On the other hand, the high preference for AI in CivEng--especially among women--highlights the interest in applying algorithms and AI systems in the design and analysis of structures. Finally, the 100% adoption of AI in MinEng reflects the relevance of this technology in the exploration and exploitation of mining resources, promoting automation and advanced data analysis. In addition, statistical tests were conducted to determine significant differences between academic programs, gender, and technology, with a significance level of \(\alpha\) = 5%. First, a chi-square test was performed to assess a significant relationship between students' gender and using Industry 4.0 technologies, utilizing data from Table 1. The null hypothesis (H\({}_{0}\)) states that there is no significant relationship between gender and the use of these technologies, while the alternative hypothesis (H\({}_{1}\)) suggests that such a relationship exists. The test results (\(\chi^{2}\) = 0.0359, df = 1, \(p\) = 0.6571) did not provide sufficient evidence to reject the null hypothesis. This indicates no statistically significant relationship between students' gender and using Industry 4.0 technologies. In other words, the findings suggest that there is no statistically significant evidence to conclude that gender influences the use of these technologies. To investigate other possible relationships, another chi-square test was performed to examine the association between students' academic programs and the type of 4.0 technologies used, based on the data in Table 1. The null hypothesis (H\({}_{0}\)) states that there is no significant relationship between the academic program and the use of 4.0 technologies, while the alternative hypothesis (H\({}_{1}\)) suggests that such a relationship does exist. The test results (\(\chi^{2}\) = 5.99, df = 2, \(p\) = 0.2021) did not provide sufficient evidence to reject the null hypothesis. This suggests that no statistically significant relationship exists between students' academic programs and the type of 4.0 technologies used. Therefore, the academic program does not significantly influence the 4.0 technology students use. Based on data from Table 2, another chi-square test was performed to evaluate whether there was a significant relationship between students' gender and their use of specific Industry 4.0 technologies (AI, IoT, and Robotics).