@inproceedings{lugini-litman-2017-predicting,
title = "Predicting Specificity in Classroom Discussion",
author = "Lugini, Luca and
Litman, Diane",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5006",
doi = "10.18653/v1/W17-5006",
pages = "52--61",
abstract = "High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students{'} claims, and retain information for longer periods of time. Previous small-scale studies have shown that one indicator of classroom discussion quality is specificity. In this paper we tackle the problem of predicting specificity for classroom discussions. We propose several methods and feature sets capable of outperforming the state of the art in specificity prediction. Additionally, we provide a set of meaningful, interpretable features that can be used to analyze classroom discussions at a pedagogical level.",
}
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%0 Conference Proceedings
%T Predicting Specificity in Classroom Discussion
%A Lugini, Luca
%A Litman, Diane
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lugini-litman-2017-predicting
%X High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students’ claims, and retain information for longer periods of time. Previous small-scale studies have shown that one indicator of classroom discussion quality is specificity. In this paper we tackle the problem of predicting specificity for classroom discussions. We propose several methods and feature sets capable of outperforming the state of the art in specificity prediction. Additionally, we provide a set of meaningful, interpretable features that can be used to analyze classroom discussions at a pedagogical level.
%R 10.18653/v1/W17-5006
%U https://aclanthology.org/W17-5006
%U https://doi.org/10.18653/v1/W17-5006
%P 52-61
Markdown (Informal)
[Predicting Specificity in Classroom Discussion](https://aclanthology.org/W17-5006) (Lugini & Litman, BEA 2017)
ACL
- Luca Lugini and Diane Litman. 2017. Predicting Specificity in Classroom Discussion. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 52–61, Copenhagen, Denmark. Association for Computational Linguistics.