The New England Journal of Statistics in Data Science

Prompted by social upheaval in 2019, Chile initiated its first foray into entirely online education. Subsequently, owing to the global pandemic in 2020, all activities underwent an unavoidable shift to digital platforms, both on a global scale and within the university context. In response to this novel distance-learning system, the university encountered and surmounted new challenges during the four semesters that were conducted in this modality. This paper examines from a spatial statistics perspective the academic performance of first-year students enrolled in basic science courses across all engineering majors at Universidad Técnica Federico Santa María (USM) in Chile. This article emphasizes students’ performance as a georeferenced variable in space and compares it with a regular semester in which lectures are conducted in a face-to-face format. In particular, we discuss (a) the spatial patterns observed in the two largest cities, (b) the social variables that are pertinent for the study, and (c) the quality and performance of conditionally autoregressive (CAR)-type processes in modeling multivariate lattice data. We also reflect on the opportunity to enhance the learning experience linked to the retention rate of freshman engineering students.