REVIEW 5 cited by
The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Classroom discourse is a core medium of instruction - analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction. The dataset can be found at https://github.com/ddemszky/classroom-transcript-analysis.
Forward citations
Cited by 5 Pith papers
-
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
SimClass is a new 391-hour simulated classroom speech dataset with game-engine babble noise; ASR fine-tuning on it beats Librispeech and TEDLIUM on real classroom test sets.
-
Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom Teaching Discourse
Classroom talk-move classifiers, fine-tuned with longer context and speaker labels, transfer to math tutoring and outperform tutoring-only training on the new SAGA22 dataset.
-
FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments
Fine-tuning pretrained speaker verification models on augmented children's speech reduces error rates in English-speaking classrooms, with ECAPA-TDNN nearly halving error on the MPT dataset.
-
Towards Actionable Pedagogical Feedback: A Multi-Perspective Analysis of Mathematics Teaching and Tutoring Dialogue
Applying off-the-shelf dialogue act and discourse relation models to math classroom and tutoring transcripts shows that non-talk-move utterances play functional roles in discourse, and are not just fillers.
-
"All that Glitters": Approaches to Evaluations with Unreliable Model and Human Annotations
Encoder models trained on noisy classroom ratings look super-human under standard concordance metrics, but generalizability, disattenuation, and hierarchical rater analyses show the apparent advantage is partly spurio...
Discussion (0). Continue with ORCID to comment.