## 6 Discussion
The contexts, the variables used, the analytical techniques used, and the objectives pursued have all been diverse. For example, [PERSON] et al. [30], through analysis of variance, correlation, and regression, found that academic performance was positively correlated with course attendance, grouping with peers in the collaborative learning environment, and the time spent on learning tasks. However, it was negatively correlated with the distance from the students' seat to the position of the teacher. They consider their findings to be important in detecting students who are at risk of failing a course. In contrast, the present study rather focuses in considering variables regarding MI and SRLAS dimensions. To examine academic performance in detail, [PERSON] et al. [60] suggest that, apart from pedagogical efforts, complementing the analysis with a specific student profile may be helpful in analyzing student performance. Information on gender, level of education of parents, their income, birth order in the family, and the current working conditions of the students, among others, may be useful. Along this idea, [PERSON] et al. [61] believe that socioeconomic factors, academic history, and personal interests also play a crucial role in predicting academic performance in developing countries. During the present research, it was found that MI multiple intelligences, which may be considered similar to learning styles, were less effective than SRLAS to predict academic performance. These results are similar to those reported by [PERSON] [31], who observed that learning styles are not decisive for predicting student academic performance. However, [PERSON]'s work shows that students' achievements are positively correlated with previous experience. The learning analytics techniques applied in this work were similar to those used by [PERSON] et al. [26]. In their research, [PERSON] et al. used main components analysis, correlation analysis, factor analysis, and clustering as exploratory techniques. They used multiple regression for their predictive analysis and did not require the Exploratory Factor Analysis because the used instruments have already been validated. The input data used by them are different than ours, since they analyzed student interaction data from Moodle, such as accesses, questionnaires, tasks, and student's age, while our database uses student profiles based on MI and SRLAS constructs. The effect of self-regulation learning on academic performance was also studied by [PERSON] et al. [18], who analyzed student statistics in asynchronous online courses using classification techniques, such as Decision Trees and Random Forests. These authors used clustering to identify groups of self-regulated, partially self-regulated, and non-self-regulated students. When observing a better academic performance of self-regulated students, they used the Random Forest classifier to deduce rules for the development of student self-regulation. The present research's findings are in line with these authors in the sense that self-regulation is an important aspect of academic success. However, unlike their work, this study included mostly engineering students, while self-regulation learning was estimated through one of the SRLAS dimensions. Another important aspect to consider in learning prediction models is the management of student anxiety. The relationship between test anxiety and academic performance has been investigated by several authors. [PERSON] et al. [32] found a small but significant inverse relationship between these two variables through basic statistics in students from different careers and universities in the United States. Similarly, [PERSON] et al. [33] reported a significant positive correlation between anxiety level and low academic performance of a large sample of engineering students in Malaysia, concluding that anxiety is an important predictor generating a detrimental effect on student academic performance.