1 citations · 1 across the 6 of their papers we have counts for
12 papers
Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham +7
Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrier. In mathematics tutoring trans…
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-…
Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale
Daryl Hedley, Doug Pietrzak, Jorge Dias +9
Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and in…
LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse
Bakhtawar Ahtisham, Kirk Vanacore, Zhuqian Zhou +2
Large Language Models (LLMs) are increasingly deployed to automatically label and analyze educational dialogue at scale, yet current pipelines lack reliable ways to detect when mod…
AI Annotation Orchestration: Evaluating LLM verifiers to Improve the Quality of LLM Annotations in Learning Analytics
Bakhtawar Ahtisham, Kirk Vanacore, Jinsook Lee +3
Large Language Models (LLMs) are increasingly used to annotate learning interactions, yet concerns about reliability limit their utility. We test whether verification-oriented orch…
Codebook-Injected Dialogue Segmentation for Multi-Utterance Constructs Annotation: LLM-Assisted and Gold-Label-Free Evaluation
Jinsook Lee, Kirk Vanacore, Zhuqian Zhou +3
Dialogue Act (DA) annotation typically treats communicative or pedagogical intent as localized to individual utterances or turns. This leads annotators to agree on the underlying a…