## 5 Analysis and Outcomes
### Analysis of Performances by Section and Type of Activity  
Comparisons between individual and team performances were useful in DL, BL and OL to discriminate certain behavioral aspects in the learning dynamics (see [37]). Notably, the trends shadow the individual performance and show how social learning could support low to mid-performing students. To analyze the individual and team behavior in terms of course performances, we compared average individual and team grades by type (Activity: teamwork or Examination: individual) and by section (Mathematics, Physics, Computational Sciences, and Challenge). Thus, in each section, the weighted average for each grade for each student was performed as a component of the entire block value. A quantile analysis was performed to analyze the behavior of performance distribution in each section. A quartile distribution was preferred for its simplicity. The data were closely conglomerated in general, thus not requiring refinement. Quartile analysis is valid for metric variables (as grade scales are) to reveal characteristics of the inner statistical distribution. The corresponding quartile distribution was constructed for the individual and team performance average of students in each section. Figure 4 presents the five limits for the quartile distribution of each measurement. Data are also directly reported in Table A1 of Appendix A. Thus, the inner space between the lines depicts the performance amplitude in each quartile (interquartile ranges, IQR). No individual examination was considered in the Challenge domain because it had a different character, oral instead of written. Note that teams conducted the activities while the examinations were individual. Figure 4: (**a**) Average grades in team activities by section and limits of quartile distribution, and (**b**) average grades in individual examinations by section and quartile distribution. Figure 3: Storyline showing attendance (blue line) and LMS accesses (orange line) in a 0–100% scale (in the last case respect to the maximum of daily accesses in the period), together with the grades average by section (Math—M, Physics—P, Computation—C, Challenge—Ch) and type (Activities—A, Examination—E, Activity Team—AT, Activity Individual—AI). For the Math and Physics sections, the zero quartiles (\(Q_{0}\)) in the Activity grades begin at zero (Figure 4a), meaning that the activities were dropped or completely failed by some students, which did not happen in Computational Sciences. Note the large dispersion in the activity grades in the Math and Challenge sections, not occurring with the same magnitude in Physics and Computational Sciences. For examinations, Figure 4b (see Table 1), shows that the Computational Sciences section has a narrower dispersion. Furthermore, in team activities, this section exhibits such narrower behavior notably. In terms of the second quartile (\(Q_{2}\), constructed with the second and fourth limits) centered in the median, there is a remarkable drop in the Challenge section of Team performances and an elevation in the Computational Sciences section for Individual performances (see [38] for interpretation of the quartile distribution). Because there was interest in the differentiation between individual and social learning to quantify the performance impact of the COVID-19 quarantine and students' learning behavior, a splitting of the performances in both arenas was performed. Individual performances lower than teamwork performances can indicate hidden low-performance students affecting the team grade. Higher individual performances than teamwork performances denote low collaboration or disorganization within the team. Furthermore, gender is commonly associated with disadvantages in Education (see [39]), so this possible differentiation factor was considered. A more in-depth view of those aspects is addressed below.