## 6 Discussion
These findings are consistent with the results obtained in this study, where anxiety is one of the eight dimensions of our SRLAS construct with a significant effect on academic performance. The negative relationship between anxiety and academic performance is found in the clustering analysis, where the clusters characterized by a low level of anxiety had a significantly higher performance than the other clusters. In addition, in the regression analysis, it was identified that anxiety is a variable with a very significant negative effect on the prediction of academic performance. Furthermore, in the present study, a high correlation between students' anxiety and their extrinsic motivation needs was also found. The detrimental role of anxiety on student academic outcomes is also noticed in the work of [PERSON] et al. [21], who uses a machine learning-based model for predicting students' performance in higher education. They also used visualizations and classification techniques to find significant factors to build a predictive model. They found that support vector machine, random forest, and naive Bayes techniques may effectively train limited samples to generate appropriate prediction performance. With regard to motivation, [PERSON] et al. [27] investigated mathematical competence in middle schools using a 14-item questionnaire. They found that the teacher-student relationship and motivation were crucial factors to achieve optimal academic performance. In addition, [PERSON] et al. [34] found that, although anxiety and motivation had a negative and a positive effect in academic performance, respectively, it was possible to moderate anxiety through motivation. Therefore, they concluded that universities should design adequate psychoeducational interventions to improve motivation and increase the performance of the students. In this regard, in the present research, two types of motivation, intrinsic and extrinsic, were included in the SRLAS construct with undergraduate students. It was identified that the intrinsic motivation was positively related to students' academic performance, while extrinsic motivation, defined as the need of students to have external motivation, presented an inverse relationship with students' academic outcomes. Consequently, the methodology proposed in this research will allow instructors to propose mechanisms to design tools for two main types of support actions. First, it will be possible to: (a) provide early warnings to increase student success, (b) build models of student behavior to predict academic performance, (c) increase self-reflection and self-awareness of responsibilities and roles in the teaching-learning process, and (d) design applications to improve on time feedback and evaluation processes. Secondly, it will also be possible to offer course recommendations in adaptive systems, providing tools to predict dropouts, thus increasing student retention, as well as making suggestions on the optimal use of educational resources. The contributions of the predictive values of the constructs used in the present work to define the students' profiles may also be combined with the results of diagnostic tests applied at the beginning of the courses to offer personalized and adaptive learning environments, both in face-to-face or online modes. Furthermore, in a next phase, we plan to determine individualized predictions by means of classification algorithms, in order to know if each student has a risk profile of failure and take the appropriate help actions at the appropriate time.