Argument mining systems often consider contextual information, i. e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction.
Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop.
Although Natural Language Processing (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.
This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants.
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.