Salome Aguilar Llanes

Salome Aguilar Llanes

Thesis Writer

Research Fields

Development Economics, Behavioral Economics

Contact Information

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Email Address saloagui@mit.edu
Personal Website ORCID
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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) interventions. 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 classroom observation. We implement a randomized controlled trial (RCT) in an online setting where university students tutored elementary and middle school students in math. We randomized the delivery of a structured SEL curriculum of ten one-hour modules, compared to a control group receiving ten one-hour history modules. The SEL group reported improved rapport between tutors and students and increased math learning by 0.0848 standard deviations. To study its impact on classroom behavior, we collected and transcribed more than 11,700 hours of tutoring sessions and applied a novel hypothesis generation method to the text. This analysis reveals that SEL-treated math classes featured a slower pace, less technical language, less frustration, more personal topics, and a less serious overall tone in the opening minutes of class. We benchmark these findings against prespecified outcomes from the prior SEL literature - engagement, classroom climate, and instructional quality - and show that the generated hypotheses capture additional variation beyond what these known measures explain. Together, these results demonstrate that SEL can significantly impact classroom behavior, relationships, and learning outcomes. They also showcase the potential of combining large-scale classroom recordings with hypothesis generation methods for better understanding of the dynamics of skill formation.
     

 

Can Single-Gender Classrooms Counteract Traditional Gender Beliefs? Evidence from a Tutoring RCT (with Bernardo García Bulle Bueno)

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.

 

 

Expanding Access to Tutoring: A Scalable Platform for Personalized Learning and Data-Driven Research

With  Bernardo Garcia Bulle Bueno, Maria Fernanda Albo Alarcón and Sebastián Guevara Cota 

Abstract

 

Expanding access to high-quality tutoring is critical for reducing educational disparities, yet scaling effective programs remains a challenge. We developed a platform that automates key logistical aspects of online tutoring, enabling large-scale implementation. Our system includes a real-time monitoring framework that tracks tutor activities. Prior research on online tutoring has shown positive effects on student learning. Building on this, we conducted a randomized controlled trial (RCT) in Mexico. We find that students assigned to tutoring improved their math scores by 0.14 standard deviations. Beyond tutoring delivery, the platform serves as a tool for research. Participating tutors upload class recordings. While this paper focuses on the tutoring intervention, we provide an overview of the platform’s potential to facilitate large-scale RCTs. We also show some basic applications of machine learning tools to our data with the aim to analyze student-tutor interactions at scale, bridging the gap between quantitative and qualitative research in education.

 


TutorUp: What If Your Students Were Simulated? Training Tutors to Address Engagement Challenges in Online Learning
Wtih Sitong Pan, Robin Schmucker, Bernardo Garcia Bulle Bueno, Fernanda Albo Alarcón, Hangxiao Zhu, Adam Teo and Meng Xia

 

With the rise of online learning, many novice tutors lack experience engaging students remotely. We introduce TutorUp, a Large Language Model (LLM)-based system that enables novice tutors to practice engagement strategies with simulated students through scenario-based training. Based on a formative study involving two surveys (N1 = 86, N2 = 102) on student engagement challenges, we summarize scenarios that mimic real teaching situations. To enhance immersion and realism, we employ a prompting strategy that simulates dynamic online learning dialogues. TutorUp provides immediate and asynchronous feedback by referencing tutor-students online session dialogues and evidence-based teaching strategies from learning science literature. In a within-subject evaluation (N = 16), participants rated TutorUp significantly higher than a baseline system without simulation capabilities regarding effectiveness and usability. Our findings suggest that TutorUp provides novice tutors with more effective training to learn and apply teaching strategies to address online student engagement challenges.

Jóvenes Ayudando a Niños y Niñas A.C. (JANN) is a nonprofit organization that aims to tackle Mexico’s learning gap through free, 100 % online tutoring.

University students sign up to participate as volunteer tutors. They are assigned a group of students to work with remotely. Group sizes range from 1 – 5 students. Tutors deliver 2 hours of tutoring per week for 1 to 24 weeks

  • Beneficiaries: 25 000 + elementary and middle-school students have received classes
  • Volunteers : 8 700 + university tutors

Co-founders: Salome Aguilar, Fer Albo, Bernardo García and Sebastián Guevara.

Star team: Guadalupe Ramírez, Brandon Moreno, Elyana Ramos and Sherry Soria.

Learn more at jann.mx.