Christopher Olshefski


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The Discussion Tracker Corpus of Collaborative Argumentation
Christopher Olshefski | Luca Lugini | Ravneet Singh | Diane Litman | Amanda Godley
Proceedings of the 12th Language Resources and Evaluation Conference

Although NLP research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of synchronous, multi-party argumentation are available. The Discussion Tracker corpus, collected in high school English classes, is an annotated dataset of transcripts of spoken, multi-party argumentation. The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio. The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others’ ideas). In addition to providing descriptive statistics on the corpus, we provide performance benchmarks and associated code for predicting each dimension separately, illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.

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Discussion Tracker: Supporting Teacher Learning about Students’ Collaborative Argumentation in High School Classrooms
Luca Lugini | Christopher Olshefski | Ravneet Singh | Diane Litman | Amanda Godley
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

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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Annotating Student Talk in Text-based Classroom Discussions
Luca Lugini | Diane Litman | Amanda Godley | Christopher Olshefski
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

Classroom discussions in English Language Arts have a positive effect on students’ reading, writing and reasoning skills. Although prior work has largely focused on teacher talk and student-teacher interactions, we focus on three theoretically-motivated aspects of high-quality student talk: argumentation, specificity, and knowledge domain. We introduce an annotation scheme, then show that the scheme can be used to produce reliable annotations and that the annotations are predictive of discussion quality. We also highlight opportunities provided by our scheme for education and natural language processing research.