## 6 Discussion
### Evaluating Tec 21 Model in the Block under the DFM  
As the institution's primary interest is the DFM's impact on the SBE in the course, then assessing this during emergency confinement is imperative. It is still one of the main interests for the author to assess course design decisions, but together with other punctual aspects emerging from the course's digital delivery. Because the Challenge is an indicator of the SBE model's success for the course as planned by the institution, this analysis focuses on it, then considers causal factors of some other performances emerging in the knowledge sections (Math, Physics, and Computation).  
For the \"before\" purpose and focusing on the data in Figure 6 and Table 1, we performed a regression analysis to describe E dependence on \(\Delta\)E, TW, and Ch variables. Two assumptions were considered (see [47]). The first one is that functional dependence is parametric (the predictors have a concrete form in such dependence); otherwise, non-parametric dependence requires extensive data sets not available in the current situation. The second one is that the dependence is barely linear, justified because short ranges are considered for E ([68, 88.1] in Table 1). The dependence on those factors is expected to be partial (due to the complexity of the behavior), so this assumption is sufficient to justify an insight about it.  
Thus, from the data in Table 2, the adjusted square correlation coefficient, \(R^{2}_{\text{adj}}\) for increasing sets of the assumed independent variables E, \(\Delta\)E, and TW was obtained. In that analysis, it was found that TW provided the highest \(R^{2}_{\text{adj}}=0.26\) compared with other single elections. It becomes maximally increased to \(R^{2}_{\text{adj}}=0.41\) by adding \(\Delta\)E (compared with adding any other two variables, one at a time). Finally, it increases to \(R^{2}_{\text{adj}}=0.43\) when considering the three variables. Other order combinations reduce \(R^{2}_{\text{adj}}\). These computations show that the primary variable of explanation is TW, followed by \(\Delta\)E, and, lastly, E with a small contribution. For the complete regression model, \(R^{2}=0.59\) is obtained. Despite being small, it becomes meaningful to establish certain explanations [47]. An expected outcome was then tested; a successful team in the single activities commonly predicts a good outcome in the Challenge resolution. A secondary factor (measured through the Examinations) is the skill dispersion in the team, notably more than the proper mean, E. Performing the associated ANOVA test [47] with \(\alpha=0.05\) for the multiple coefficients of correlation and the data shown in Tables 2 and 4, we obtain \(3.79=F>F_{\text{critical}}=0.06\), thus indicating a clear underlying correlation.  
Some aspects found in the analysis commonly escape teachers in the daily development and delivery of the class. Absorbed by their own sections, teachers can lose attention to their students' success in the Challenge development (despite the expectation they should resolve it with little assessment and feedback). Nevertheless, the analysis showed some correlation between the Challenge performance and the sessions' work, which, together with the students' opinion survey, reflects the students' need for broad accompaniment from the professors, even outside the sections they teach.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ANOVA** & **d.f.** & **Sq. sum** & **Sq. av.** & \(F\)**value** & \(F_{\text{critical}}\) \\ \hline Regression & 3 & 698.46 & 232.82 & 3.79 & 0.06 \\ Residuals & 8 & 491.79 & 61.47 & & \\ Total & 11 & 1190.25 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA test for the absence of correlation between Ch grades and variables E, \(\Delta\)E, and TW as null-hypothesis.