Abstract
The overarching goal of this comprehensive meta-methods research study, which entailed a thorough integration of multiple methods research approaches and mixed methods research approaches, was to explore the impediments faced by students at a Chilean university. These challenges obstructed their effective engagement in remote learning during the exigent circumstances of emergency remote teaching and learning brought about by the COVID-19 pandemic. Specifically, a total of 354 students participated by completing an online questionnaire. WordStat 8.0.29 topic modeling, involving a crossover mixed analysis method, which was utilized to analyze the students’ open-ended responses, revealed the following four challenge themes that categorized the array of challenges faced by the students: Motivation, Time Management, Mental Health, and Learning Challenges. Further, a canonical correlation analysis revealed two statistically significant canonical functions that represented the multivariate relationship between four demographic variables (i.e., gender, age, child status, work status) and the four emergent challenge themes. Applying latent class analysis, which involves the process of qualitizing, yielded three distinct student profiles: (a) a cluster characterized by Time Management cluster, (b) another cluster centered around Mental Health cluster, and (c) a cluster interwoven with Motivation and Learning Challenges. Deconstructing these themes based on demographic variables led to the discernment of subgroups that are particularly vulnerable with respect to each specific challenge. The ramifications of these findings, in conjunction with other insights, are thoroughly explored and discussed.
Author Information
Silvina Zapata, Universidad Andrés Bello, Chile
Anthony Onwuegbuzie, University of Johannesburg, South Africa
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