Edge Computing Infrastructure and Digital Learning Autonomy in Latin American Higher Education

This study examines how edge computing infrastructure relates to digital learning autonomy in higher education institutions across Colombia, Peru, and Venezuela between 2019 and 2024. Using secondary data from government telecommunications sources and network performance repositories, the research applies ordinal logistic regression, confirmatory factor analysis, and mixed-effects modeling to identify general patterns rather than precise causal mechanisms. The results show that regions with more edge computing nodes and higher levels of local traffic processing tend to report slightly better digital autonomy scores among students, although the effect sizes remain modest. These outcomes suggest that edge computing works mainly as an enabling condition that reduces latency and stabilizes digital platforms, helping students engage more independently with learning tasks when other pedagogical and institutional factors are also supportive. While the associations are consistent, the study recognizes limitations related to proxy measurements, uneven data quality, and contextual differences across countries. Overall, the findings provide a basic empirical contribution to discussions about distributed computing and educational digitalization in developing regions.