Ana Trindade Ribeiro
Senior Research Associate, SAL Policy
Bio
Ana Trindade Ribeiro is a Senior Research Associate at SCALE Initiative at Stanford University, affiliated with the GenAI in Education Hub and the National Student Support Accelerator. Her research applies Natural Language Processing and Causal Inference methods to investigate effective practices in personalized instruction and develop tools to increase student engagement and learning. Ana is passionate about research that can support evidence-based decisions for policy and institutions. She holds a PhD in Economics and Education from Stanford University, an MA in Economics from the University of Sao Paulo, and a BS in Economics from PUC-Rio.
Current Role at Stanford
Senior Research Associate at SCALE Initiative
Education & Certifications
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MSc, University of Sao Paulo, Economics (2017)
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BA, Pontifical Catholic University - Rio de Janeiro, Economics (2013)
Projects
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Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
Location
stanford university
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The gender gap in test performance explained by behavioral differences under time pressure
Although high-stakes tests are designed to be objective and unbiased, gender differences in performance have been widely documented across many years and countries, consistently showing women to be underrepresented at the top of the performance distribution, where the best opportunities are granted. This paper uses an experimental approach to measure gender differences in performance under varying degrees of time pressure, and differences in thoroughness in the test-taking environment, which is a behavior more often associated with women than men and may not be optimal in a time-constrained setting. By varying the time limit condition, this experiment investigates to what extent test-taking time use and thoroughness can explain the gender gap in performance. In this experiment, I find that the gender when time is strictly constrained is significantly wider than when time is unconstrained. The gender difference in time use for the constrained condition implies that men see on average one question more than women, which approximates the performance gap.
Location
Stanford, CA
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Long-term outcomes of affirmative action - Evidence from a law school in Brazil
(Joint with Fernanda Estevan)
This paper uses one of the first quota policies implemented in Brazil, at Flagship State University (UERJ), to separately investigate the long-term effects for applicant who benefited from the Affirmative Action policy and non-AA applicants displaced by it. We focus on applications to the undergraduate law major at UERJ for three reasons. First, UERJ’s application process allows us to identify applicants to either AA or non-AA slots, and, among them, those who were offered admissions. Second, this is a highly selective undergraduate program. A 30-40 point (out of 100) difference in the cutoff scores between AA and non-AA shows that AA applicants were subjected to a much lower bar for admissions. Third, a high-stakes post-college exam (lawyers’ licensing process) enables tracking applicants intothe law career after college. In addition, we combine government data, including employment information (RAIS), firm ownership, and graduate degrees, along with online scraped data for
the lawyer licensing exam, internship applications, and college graduation. For applications
between 2006-2011, we are able to track about 87\% of AA and 79\% of non-AA applicantsaround each group-specific cutoff across outcomes. Our results suggest that, for beneficiaries, this AA quota policy increases the probability of graduating from college from 41\% to about 80\%, becoming a certified lawyer from 31\% to about 70\% , and being employed as such from13\% to about 30\%. We find that applicants displaced by the policy do not appear to be negatively impacted, possibly because they could’ve been admitted to other quality universities. We estimate that non-AA applicants both slightly above (admitted) and below (displaced) thecutoff have a 71\% chance of graduating from college, about 70\% of becoming a licensed lawyer, and 25\% of being employed as such. We interpret the net effect of this policy to be positive, expanding opportunities for the less privileged without any significant direct impacts on others.Location
Stanford, CA
Work Experience
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Research Assistant for Professor Eric Bettinger, Stanford University (October 2017 - June 2022)
I was responsible for performing data analysis for multiple research projects and managing field experiments (RCTs and A/B tests):
- Generated descriptive statistics to inform the viability of projects, which supported decision-making;
- Applied causal inference methods, including unsupervised machine learning methods to estimate heterogeneity in the treatment effect of interventions, generating research results.
- Developed and maintained a partnership with a governmental institution to facilitate the implementation and evaluation of a large-scale intervention, delivering a sample size of 40,000 participants to the experiment;
- Designed a dynamic model to visualize the trade-offs between statistical power and financial needs, which was used to define implementation priorities and funding procurement;
- Procured and negotiated with potential service providers and financial supporters, securing more than US$180,000 in additional funding.Location
Stanford, CA
All Publications
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Differences in Time Usage as a Competing Hypothesis for Observed Group Differences in Accuracy with an Application to Observed Gender Differences in PISA Data
JOURNAL OF EDUCATIONAL MEASUREMENT
2024
View details for DOI 10.1111/jedm.12419
View details for Web of Science ID 001357221100001