Abstract:

The COVID-19 pandemic affected the lives of millions of people worldwide, hundreds of aspects were involved in many ways, and one of the most commented and analyzed was the education systems. A significant percentage of schools used the face-to-face educational model, and in a matter of weeks, almost any school had to move to a remote educational one to ensure the integrity of their students, professors, and staff.

In Mexico, this phenomenon was not an exception. At the beginning of the pandemic, all the schools, from primary to superior level, developed different programs which allowed them to continue with teaching.

For this purpose, the Mexican Universidad Autónoma Metropolitana (UAM) developed the Emergent Program of Remote Teaching (PEER for its acronym in Spanish). During almost two years, which means six scholar periods at UAM, students took classes using different remote teaching methods, like online classes or reviewing digital material.

The opinions, not only at UAM, but in all the educational systems in Mexico (and surely in many countries), were that students would be significantly affected in their academic performance, even more in engineering programs, where practices, in special laboratories, were not available.

As the effects of the pandemic decreased, the educational system, including UAM, returned to the face-to-face educational model; however, analyzing the academic performance of the remote imparted classes and comparing it with the ratings of the traditional model results of interest for measuring the impact of the PEER schema.

Considering this, this work presents the analysis of some statistics related to the number of enrolled students in different courses of an engineering program and the percentage of approved and not approved students per course. The gathered data correspond to two scholarly periods before the pandemic and six scholarly periods during the PEER schema.

Results are presented using some Information Visualization techniques and show the impact of the implemented programs at UAM. Also, these results would serve as a first approach for posterior analysis, considering, for example, the performance of students in later courses, comparing their performance after approving a previous one in the face-to-face or the distance educational model.

Keywords:

Educational analytics, distance education, academic performance, approval ratings, online education impact.