# Argument Component Classification for Classroom Discussions

arXiv: https://arxiv.org/abs/1909.03022 · HTML: https://mlsanity.com/paper/1909.03022/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Analyzing Large Language Models for Classroom Discussion Assessment](https://mlsanity.com/paper/2406.08680/) (2024)
- [Contextual Argument Component Classification for Class Discussions](https://mlsanity.com/paper/2102.10290/) (2021)
- [Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms](https://mlsanity.com/paper/2102.10293/) (2021)
- [Winning with Less for Low Resource Languages: Advantage of Cross-Lingual English_Persian Argument Mining Model over LLM Augmentation](https://mlsanity.com/paper/2511.20872/) (2025)

## Abstract (arXiv)

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. We show that an existing method for argument component classification developed for another educationally-oriented domain performs poorly on our dataset. We then show that feature sets from prior work on argument mining for student essays and online dialogues can be used to improve performance considerably. We also provide a comparison between convolutional neural networks and recurrent neural networks when trained under different conditions to classify argument components in classroom discussions. While neural network models are not always able to outperform a logistic regression model, we were able to gain some useful insights: convolutional networks are more robust than recurrent networks both at the character and at the word level, and specificity information can help boost performance in multi-task training.