## 4 Results  
### Descriptive Statistics  
The descriptive statistics are presented in Table 2. The table includes three learning modes: traditional, STEM-based, and STEAM-based. For each educational mode, the mean, standard deviation, skewness, and kurtosis of the students' answers and tutor estimations were calculated. The authors displayed descriptive statistics according to the educational mode without dividing the mode into particular questions. For the sake of simplicity, clarity, and integrity, statistical criteria were calculated for the educational mode to emphasize the crucial role of active learning methods and significant differences compared to the traditional one. To avoid the appearance of anomalies in the questionnaire that could lead to confusion from subjective answers by secondary school students or tutors' expertise, the authors use average values for the particular education mode. External factors like mood, behavior, tiredness, thermal comfort, mental agility, and the respondent's self-perception could also influence the completeness and sufficiency of the questionnaire. Moreover, the authors acknowledge the possibility of inconsistencies in specific educational modes, which could \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Sensor-Based** & **Intrinsic Relevance** & **Creativity** \\  & **Knowledge (Q1–Q5)** & **(Q6–Q10)** & **(Q11–Q15)** \\ \hline \multicolumn{3}{l}{_Traditional course_} \\ Mean & 22.59 & 29.19 & 23.23 \\ Std. Dev & 17.08 & 16.74 & 18.65 \\ Skewness & 2.19 & 1.71 & 1.55 \\urtosis & 5.37 & 3.04 & 1.74 \\ \hline \multicolumn{3}{l}{_STEM course_} \\ Mean & 58.34 & 53.80 & 49.43 \\ Std. Dev & 12.15 & 13.59 & 12.52 \\ Skewness & 1.13 & 0.80 & 0.13 \\ Kurtosis & 1.45 & 1.48 & 0.34 \\ \hline \multicolumn{3}{l}{_STEAM course_} \\ Mean & 75.10 & 86.09 & 77.93 \\ Std. Dev & 9.82 & 9.13 & 13.78 \\ Skewness & \(-0.46\) & \(-0.89\) & \(-1.58\) \\ Kurtosis & \(-0.14\) & 2.98 & 5.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics broken down by educational mode. lead to misleading and biased results and blur the overall context of the research. Overall, the authors aim to shed more light on the educational mode than on the particular questions contained. The analysis imposes a non-normal data distribution when skewness and kurtosis are, respectively, out of the range of \(\pm 1\) and \(\pm 3\). Therefore, the normality assumption does not matter and is unimportant in the presented case study [94]. The normality assumption is particularly important in statistical analysis. In practice, the violation of this assumption can lead to biased, unreliable, and inaccurate results. Some statistical treatments deal with the violation of the normality assumption. One of the most common ways is to apply transformations to the data (logarithmic or square root). Another way is to use non-parametric methods that do not assume any particular distribution of the data. Finally, the outlier detection and removal procedure should be carefully applied. Outliers can significantly blur the normality of the data. Therefore, clustering validation analysis, particularly using majority-based decision fusion methods, has been chosen as the optimal and efficacious tool to lead in quantitative analysis. The analysis of the overall questionnaire in all three educational modes revealed the highest mean score of 86.09 within the STEAM sensor-based active learning courses in the intrinsic relevance factor. The lowest overall mean score within the particular educational mode is identified in the traditional teaching/learning mode with respect to the sensor-based engineering knowledge of secondary school students in out-of-school extra-curriculum settings (22.59). With respect to the educational mode, the pairwise analysis of the mean scores identified the dominance of the STEAM active learning method over the STEM and traditional forms of education in all factors, including learning outcomes, intrinsic relevance, and creativity.