THE USE OF LEARNING ANALYSIS TO SUPPORT SELF-REGULATION OF LEARNING

With the advancement of online teaching that took place through the need for democratization in access to education, in addition to the global pandemic of COVID- 19 that demanded the closure of several educational institutions. This environment has become increasingly present in the lives of students. However, some problems are faced, highlighting the dropout of students due to lack of support, doubts, and pedagogical difficulties, but mainly due to the difficulty of teachers to assist these courses due to their workloads. This evasion generates individual and institutional economic losses. Thus, the purpose of this work is to create a data visualization and personalized recommendations for students, to support their self-regulation of learning. The system will include the student within a process that will help his learning through data visualization, which will present the student's status in relation to the goals established by the teacher. Goals regarding interactions and student performance in relation to available learning resources. Also based on the status, we have personalized recommendations, that is, suggestions for actions that will help the student achieve the expected goals or keep them engaged. The proposal will be evaluated through a plausible case of a student's situation, where the evaluators will be asked if these views would help to identify their status, the intended learning goals, and if the recommendations would help them to get out of the point where they are reach the point where it should be, thus evaluating the evaluators' perceptions of views and recommendations. As for the expected results, the proposal aims to improve the quality of learning and reduce the feeling of abandonment in online environments and can guide you in the process of self-regulated of learning.