Understanding Gender Dynamics and How the Learning Process is Affected by Tracking Students: A Tutoring Intervention

The learning crisis caused by the COVID-19 pandemic is unprecedented. As governments seek to address the learning loss, so far, we know tutoring has been a widely proposed solution because it has proven to have a big positive effect on learning. We propose a randomized evaluation which can contribute to the literature of tutoring by eliciting whether different group structures modify how teachers’ stereotypes affect students. This will constitute a step into disentangling the mechanism and generating policy that curbs this effect. Our design will also find effects of students sharing the gender with each other, as well as other covariates important in the Mexican context. Additionally, by using machine learning techniques to analyze the recordings of the tutoring sessions we can begin to disentangle how gender dynamics shape the motivation of students as well as some of the determinants of learning in the classroom.