4 papers
From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Reasoning
Vedant Khatri, Anthony Cusimano, Zachari Swiecki +3
Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions, but the presence of multipl…
Enhancing LLM-Based Data Annotation with Error Decomposition
Zhen Xu, Vedant Khatri, Yijun Dai +4
Large language models offer a scalable alternative to human coding for data annotation tasks, enabling the scale-up of research across data-intensive domains. While LLMs are alread…
What do you say? A pilot study investigating student responses in Data Driven Classroom Interviews
Jaclyn Ocumpaugh, Zhanlan Wei, Amanda Barany +4
Data that contextualizes student interactions with online learning systems can be challenging to obtain. This study looks at the rhetorical strategies of a novel method for conduct…
The Quick Red Fox gets the best Data Driven Classroom Interviews: A manual for an interview app and its associated methodology
Jaclyn Ocumpaugh, Luc Paquette, Ryan S. Baker +14
Data Driven Classroom Interviews (DDCIs) are an interviewing technique that is facilitated by recent technological developments in the learning analytics community. DDCIs are short…