As part of my work as researcher and software developer at the Information Technology Center (CTI) at ESPOL university in 2018, I participated in the implementation of visualization tools for processed data applying learning analytics techniques. The data corresponds to all Uruguayan students from public institutions during their first three years of secondary education collected by the Uruguayan National Administration of Public Education ANEP. Three countries participated in this initiative, Uruguay, Brazil, and Ecuador. I was part of the Ecuadorian team for developing a dashboard to understand the trajectories of students. We met in Montevideo, Uruguay to set the methodologies and bases for the remote work to do in our respective countries.
Work session in Montevideo, Uruguay 
Henrique Lemos (L) and Luiz Macarini(R), Brazilian Team 
From left to right, Ricardo Maya (Ecuador), Pedro Lucas (Ecuador), Virginia Rodes (Uruguay) 
From left to right, José Fager (Uruguay), Luiz Macarini(Brazil), Ricardo Maya (Ecuador), Pedro Lucas (Ecuador) 
Fom left to right, Pedro Lucas (Ecuador), Ricardo Maya (Ecuador), Luiz Macarini (Brazil), Henrique Lemos (Brazil)
The Dashboard
Together with my workmate Ricardo Maya, we implemented a dashboard to show the data processed by the algorithms used by the Henrique and Luiz (Brazilian team). We used Node.js to implement a light back-end to extract the processed data from a curated database built by Henrique and Luiz, but our main focus was to built the front-end to show the data through understandable charts; in this case, we used d3.js JavaScript library and typical solutions with HTML, CSS and pure JavaScript for the final result. The next image depicts the main page of the visualizations.
The organization of the dashboard is based on like-cards sections with different visualizations according to the data to be shown and the requirements from the Uruguayan educational institution, The next set of images summarizes each visualization that is part of the dashboard.
Trajectory of students in three years 
Stats about courses and attendances regarding students 
Performance comparison in terms of courses according to some filters 
Relationships between grades and attendances 
Trajectory of students based on how much of them were promoted, or failed courses in the three years 
Students promoted or failed according to courses 
Comparisons of students regarding cohorts and their final state 
Visualization of a risk prediction based on learning analytics strategies