Recording the Impact of Social and Emotional Learning (with Fernanda Albo and Bernardo García Bulle Bueno)
Abstract
Social skills have been shown to predict earnings, teamwork, and productivity, and they are in increasing demand in the labor market. Schools play a central role in fostering these skills, increasingly through social and emotional learning (SEL). Despite the widespread adoption of SEL programs, evidence on their effects on classroom behavior is limited, as most studies rely on self-reports, survey measures, or, in a few cases, classroom observation. We implement a randomized controlled trial in an online setting where university students tutored elementary and middle school students in math. We randomized the delivery of an SEL intervention of ten one-hour modules, compared to a control group receiving ten one-hour history modules. SEL improved reported rapport (affective relationship) between tutors and students and increased math learning by 0.0495 standard deviations. To study its impact on behavior, we collected and transcribed more than 18,000 hours of tutoring sessions and applied a novel hypothesis-generation method to the text. This analysis reveals that SEL-treated math classes featured more active listening, less formal exchanges, simpler language, and more discussion of personal topics at the start of class. Together, these results demonstrate that SEL can significantly impact classroom behavior, relationships, and learning, and showcase the power of large-scale classroom recordings for understanding the dynamics of skill formation.
Abstract
This paper explores how group composition moderates the negative impact of traditional gender beliefs (TGB) on girls’ learning. While prior research has documented the detrimental effects of gender-biased attitudes, the mechanisms driving these outcomes remain poorly understood. We conducted a randomized controlled trial (RCT) in which students were assigned to either single-gender or mixed-gender tutoring groups, and tutors’ gender beliefs were independently measured. We find that girls placed in mixed-gender groups learned significantly less when their tutor held traditional gender beliefs. In contrast, girls in single-gender groups were not negatively affected by tutors with TGB. These findings suggest that differential treatment of boys and girls may underlie the observed learning gap, and point toward group composition as a key lever for mitigating the effects of gender bias in educational interventions.
Good Vibes in Class: A Tool to Detect Which Emotions Lead to More Learning (with Fernanda Albo, Bernardo García Bulle Bueno and Tobin South)
Abstract
We present a method to characterize the classroom environment through emotion detection from audio recordings. Using machine learning tools we build an emotion classifier using MFCC features of labeled voice clips and apply it to slices of more than 1,500 online class session records. We find that higher measurements of high-intensity emotions were significantly correlated with higher Teacher Value Added (TVA) estimates, determined using Math test scores of students before and after receiving tutoring. Secondly, we found that attendance metrics in the second class were highly correlated to the class environment in the first class. Finally, we found that higher-skilled tutors progressively increased high-intensity emotions as they had more sessions with their students.