34 citations · 54 across the 7 of their papers we have counts for
7 papers
Does Algorithmic Uncertainty Sway Human Experts? Evidence from a Field Experiment in Selective College Admissions
Hansol Lee, AJ Alvero, René F. Kizilcec +1
Algorithmic predictions are inherently uncertain: even models with similar aggregate accuracy can produce different predictions for the same individual, raising concerns that high-…
Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time
Hansol Lee, Jason B. Cho, David S. Matteson +1
We present D-BIRD, a Bayesian dynamic item response model for estimating student ability from sparse, longitudinal assessments. By decomposing ability into a cohort trend and indiv…
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions
Hansol Lee, René F. Kizilcec, Thorsten Joachims
A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized t…
Should College Dropout Prediction Models Include Protected Attributes?
Renzhe Yu, Hansol Lee, René F. Kizilcec
Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used…
Look at Me When I Talk to You: A Video Dataset to Enable Voice Assistants to Recognize Errors
Andrea Cuadra, Hansol Lee, Jason Cho +1
People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video d…
Algorithmic Fairness in Education
René F. Kizilcec, Hansol Lee
Data-driven predictive models are increasingly used in education to support students, instructors, and administrators. However, there are concerns about the fairness of the predict…
Evaluation of Fairness Trade-offs in Predicting Student Success
Hansol Lee, René F. Kizilcec
Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algor…