@inproceedings{lugini-etal-2020-discussion,
title = "Discussion Tracker: Supporting Teacher Learning about Students{'} Collaborative Argumentation in High School Classrooms",
author = "Lugini, Luca and
Olshefski, Christopher and
Singh, Ravneet and
Litman, Diane and
Godley, Amanda",
editor = "Ptaszynski, Michal and
Ziolko, Bartosz",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics (ICCL)",
url = "https://aclanthology.org/2020.coling-demos.10",
doi = "10.18653/v1/2020.coling-demos.10",
pages = "53--58",
abstract = "Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop. To address this, we have developed Discussion Tracker, a classroom discussion analytics system based on novel algorithms for classifying argument moves, specificity, and collaboration. Results from a classroom deployment indicate that teachers found the analytics useful, and that the underlying classifiers perform with moderate to substantial agreement with humans.",
}
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%0 Conference Proceedings
%T Discussion Tracker: Supporting Teacher Learning about Students’ Collaborative Argumentation in High School Classrooms
%A Lugini, Luca
%A Olshefski, Christopher
%A Singh, Ravneet
%A Litman, Diane
%A Godley, Amanda
%Y Ptaszynski, Michal
%Y Ziolko, Bartosz
%S Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F lugini-etal-2020-discussion
%X Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop. To address this, we have developed Discussion Tracker, a classroom discussion analytics system based on novel algorithms for classifying argument moves, specificity, and collaboration. Results from a classroom deployment indicate that teachers found the analytics useful, and that the underlying classifiers perform with moderate to substantial agreement with humans.
%R 10.18653/v1/2020.coling-demos.10
%U https://aclanthology.org/2020.coling-demos.10
%U https://doi.org/10.18653/v1/2020.coling-demos.10
%P 53-58
Markdown (Informal)
[Discussion Tracker: Supporting Teacher Learning about Students’ Collaborative Argumentation in High School Classrooms](https://aclanthology.org/2020.coling-demos.10) (Lugini et al., COLING 2020)
ACL