Abstract

This comprehensive meta-methods research study aimed to investigate the challenges hindering effective remote learning for students at a Chilean university during the COVID-19 pandemic. The study involved 354 participants who completed an online questionnaire. The analysis, using WordStat 8.0.29 topic modeling, identified four challenge themes: Motivation, Time Management, Mental Health, and Learning Challenges. Canonical correlation analysis revealed statistically significant relationships between demographic variables (gender, age, child status, work status) and the emergent challenge themes. Latent class analysis produced three distinct student profiles focused on Time Management, Mental Health, and an interwoven Motivation and Learning Challenges cluster. By deconstructing these themes based on demographic variables, vulnerable subgroups were discerned for each specific challenge. This research provides nuanced insights into the multifaceted barriers faced by students during the pandemic, offering a foundation for tailored interventions and future research. The study’s integration of various research approaches contributes to a more comprehensive understanding of the complex interplay between demographic factors and challenges, emphasizing the need for personalized strategies to address the diverse needs of students in a remote learning environment.