## 6 Discussion
Cluster 3, with a slightly better average final grade, is also the group with the lowest values in all dimensions, whereas Cluster 4, with the lowest average grade, did not provide a suitable pattern leading to sound conclusions for its MI dimensions (Tables 3 and 4, and Figure 5). This is partially due to the fact that the differences in grades among the clusters obtained were rather small and statistically insignificant. Therefore, the results of this research suggest that the MI construct alone lacked sufficient strength to clearly explain the differences between the grades. This conclusion is consistent with findings of other authors in the sense that there is not a simple correlation between MI dimensions and student academic performance. In fact, in the past decades, the relationship between MI dimensions and academic performance has been considered a field of research among educators from various areas at different academic levels. It has been argued that a simple correlation is lacking among these variables [55; 56; 57; 58]. Notably, [PERSON] [59] suggests that, apart from clearly defining the MI in the surveys, it is also a good idea to ask senior students to participate in the study, since they have gone through the entire academic engineering spectrum. [PERSON]'s work indicates that most students had some level of mixed MI, except the musical one. Whereas the logical-mathematical and linguistic skills were found as the most influential dimensions, musical and body-kinesthetic intelligences were the ones perceived with the least applicability to predict academic performance. Regarding the results of the SRLAS clustering, it can be appreciated that Cluster 1, with a significant highest average grade (Table 5), has the highest _IntMot_ and _IntProc_ values, relatively high values for _SelfReg_, _SocInt_, _FitMood_, and _InfSearch_, but the smallest _Anx_ and _ExtMot_ values (Figure 6b). On the other hand, Cluster 3, with the lowest average grade, has high values for all dimensions, including _Anx_ and _ExtMot_. Cluster 4, with an intermediate average grade, presents relatively small values for all dimensions. Finally, Cluster 2, with an intermediate average grade, reveals intermediate values for all the dimensions. Therefore, it is suggested that a combination of relatively high values of _IntMot_, _InfProc_, _InfSearch_, _SocInt_, _SelfReg_, and _FitMood_, on the one hand, combined with low values of _ExtMot_, and _Anx_, on the other hand, can promote better academic performance. Likewise, students presenting high levels of anxiety (_Anx_) and the need for external motivation (_ExtMot_) may face difficulties in achieving appropriate learning outcomes. This assertion is somehow validated by the teaching experience of the authors and the expectations from such student behavior. To reinforce and complement the information obtained from the clustering analysis, a regression analysis was performed. From the regression equation obtained from the MI dimensions (Equation (2)), it was found that logical-mathematical intelligence had a critically positive impact on the student's final grades, as expected, the sample being comprised mostly of engineering students. In addition, from the regression equation for the SRLAS construct (Equation (3)), it was identified that students' self-regulation also had an important positive impact on their final grades, while students' anxiety and the need for external motivation shared a negative impact on students' outcomes. Overall, taking the MI and SRLS constructs together in the regression analysis (Equation (4)), it can be concluded that the most important dimensions that presented a positive relationship with academic performance are the logical-mathematical and self-regulation, while the students' anxiety has the most negative impact on academic performance. There have been multiple efforts to predict student academic outcomes in order to improve their academic performance.