Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.20547 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911286580740096 |
|---|---|
| author | Mim, Farjana Sultana Aeron, Shuchin Miller, Eric Wendell, Kristen |
| author_facet | Mim, Farjana Sultana Aeron, Shuchin Miller, Eric Wendell, Kristen |
| contents | Identifying discourse features in student conversations is quite important for educational researchers to recognize the curricular and pedagogical variables that cause students to engage in constructing knowledge rather than merely completing tasks. The manual analysis of student conversations to identify these discourse features is time-consuming and labor-intensive, which limits the scale and scope of studies. Leveraging natural language processing (NLP) techniques can facilitate the automatic detection of these discourse features, offering educational researchers scalable and data-driven insights. However, existing studies in NLP that focus on discourse in dialogue rarely address educational data. In this work, we address this gap by introducing an annotated educational dialogue dataset of student conversations featuring knowledge construction and task production discourse. We also establish baseline models for automatically predicting these discourse properties for each turn of talk within conversations, using pre-trained large language models GPT-3.5 and Llama-3.1. Experimental results indicate that these state-of-the-art models perform suboptimally on this task, indicating the potential for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding Mim, Farjana Sultana Aeron, Shuchin Miller, Eric Wendell, Kristen Computation and Language Identifying discourse features in student conversations is quite important for educational researchers to recognize the curricular and pedagogical variables that cause students to engage in constructing knowledge rather than merely completing tasks. The manual analysis of student conversations to identify these discourse features is time-consuming and labor-intensive, which limits the scale and scope of studies. Leveraging natural language processing (NLP) techniques can facilitate the automatic detection of these discourse features, offering educational researchers scalable and data-driven insights. However, existing studies in NLP that focus on discourse in dialogue rarely address educational data. In this work, we address this gap by introducing an annotated educational dialogue dataset of student conversations featuring knowledge construction and task production discourse. We also establish baseline models for automatically predicting these discourse properties for each turn of talk within conversations, using pre-trained large language models GPT-3.5 and Llama-3.1. Experimental results indicate that these state-of-the-art models perform suboptimally on this task, indicating the potential for future research. |
| title | From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.20547 |