## 6 Discussion  
The PCA biplots demonstrate the relative independence of several of the MI (Figure 3a) and SRLAS (Figure 3b) dimensions, which may help to interpret the characteristics of students of a given section. However, it is important to emphasize that each construct is composed of eight dimensions, whereas the 2D biplot only enables the visualization of two composite dimensions (i.e., principal components). The two corresponding principal components for MI and SRLAS constitute 49% and 60%, respectively, of their total inertia. In other words, the main planes of the MI and SRLAS contain 49% and 60%, respectively, of the total information that can be obtained from the eight dimensions of each construct. Therefore, using only the distribution of students on the biplots to interpret the dimensions of the entire sample does not provide yet by itself enough information for teachers to implement the most appropriate pedagogical actions for their students. Even though MI dimensions may be considered more related to cognitive indicators, and SRLAS dimensions to behavioral aspects of the student, our results are not meant to provide an elaborated dashboard to monitor and evaluate the actual performance of a given student regarding a particular task. We do not yet have documented interventions derived from this basic dashboard (Figure 2). It provides only a first \"picture\" of the student profile regarding the 16 considered dimensions. To measure the actual effect of the MI and SRLAS profiles on the academic performance of the student separately is a complicated task, since actual learning is not limited to the learning process, and the final academic performance is also influenced by other complex factors, such as family environment, personal feelings and student personality. To better track the effect of cognitive and behavioral learning analytics (LA), several authors have worked out cognitive and/or behavioral dashboards to assist in the learning process [50; 51; 52]. According to [PERSON] and [PERSON] (2021) [51], the key objective of a behavioral LA dashboard is to gather data in a single repository, from multiple channels and networks, used to generate context models to provide students with customized input and personal recommendations. In the case of cognitive LA dashboard, four levels are considered: (a) Description: Observing events and other data to obtain a detailed picture of a student's activity; (b) Diagnosis: Descriptive elements needed to evaluate an outcome; (c) Prediction: Set likely outcome based on certain elements; and (d) Recommendations: Set how to achieve a desired learning outcome result from a specific element. [PERSON] et al. [50] implemented a system of dashboards that allow the student and the teacher to continuously monitor the academic and behavioral status of the students, as well as the evolution of their academic performance regarding a given task. They consider the regulatory mechanisms underlying the learning processes to provide the student with an effective feedback (epistemic, corrective and/or suggestive) to advance efficiently and effectively in the learning cycle, including aspects of self-regulation controlled by the student. The most comprehensive feedback includes both cognitive and conceptual aspects. However, the detailed mechanisms for user intervention in feedback remains challenging. In this sense, [PERSON] and [PERSON] (1998)'s [53] \"backward instructional design\" provides richer opportunities for tracking the whole learning processes. In addition, recent research shows increased interest in exploring biofeedback opportunities based on multi-modal data collected from various wearable sensors and audio/video streams [54]. Considering the MI clustering process alone, it is difficult to clearly identify the dimensions that exerted an important influence on academic performance.