In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners'problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract such information from dialogue for a variety of downstream tasks, including knowledge tr...
Clayton Cohn, J. Fonteles, Kirk P. Vanacore et al.· 0 citations
For learning analytics, these findings identify impasse depth and type as observable, turn-level dialogue signals that analytics can use to trigger graduated, state-sensitive assistance in real time.
Bakhtawar Ahtisham, Kirk P. Vanacore, Alessandra O. Napoli et al.· 0 citations
This work shows that LLMs can be used to strategically target expert attention, shortening months of codebook revision to days without sacrificing labeling performance.
Ze-Yu He, Zhu-Qian Zhou, Kirk P. Vanacore et al.· 0 citations
This work examines whether requiring students to justify decisions to accept or reject AI suggestions can mitigate uncritical uptake in academic writing and reveals superficial engagement in the justification task and gaps in metacognitive monitoring and domain knowledge.
Yan Tao, Jennifer Meyer, René F. Kizilcec· 0 citations
This work proposes a fully local cascade framework that reframes de-identification from open-ended entity recognition to constrained privacy triage, and evaluates three reviewer configurations against same-family LLM-only baselines and a commercial API on math tutoring transcripts from two large platforms.
Hao-Chen Zhang, Zhu-Qian Zhou, Kirk P. Vanacore et al.· arXiv.org· 0 citations
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