Association between self-regulation of learning, forced labor insertion, technological barriers, and dropout intention in Chile

Abstract

Early dropout and retention of students are critical problems in both secondary and higher education. Existing models that predict the intention to drop out require the incorporation of complex variables strongly related to student success, such as self-regulated learning. Moreover, new possible predictors have emerged in the context of a pandemic. This study set out to validate scales that measure the phases of self-regulation of learning in Chilean secondary school students and determine the association between self-regulation, forced labor insertion, technological barrier, and intention to quit during COVID-19. An instrumental design was carried out, where 251 students participated, and a cross-sectional predictive design with a sample of 171. Results showed adequate psychometric properties in assessment scales for self-regulation. Furthermore, the logistic regression model carried out to predict the dropout intention was significant. The final model showed that external causal attributions, planning self-evaluation, forced labor insertion, and technological barriers were significant predictors, achieving a success rate of 84.8%. In conclusion, although many factors are considered in dropout intention models, this study incorporated self-regulation skills that can be promoted in students and systematically integrated into school programs to help reduce dropout rates in secondary education, therefore contributing to a successful transition to higher education.