Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion

TL;DR

This paper explores the use of NLP techniques to automatically score classroom discussion quality, aiming to provide scalable assessment tools for educational research and practice.

Contribution

It introduces a method for applying NLP to automatically evaluate discussion quality using annotated classroom transcripts, demonstrating promising results on limited data.

Findings

Some rubrics achieved encouraging scoring accuracy

Different NLP approaches perform better for different rubrics

Limited data impacts the overall performance of the models

Abstract

Rigorous and interactive class discussions that support students to engage in high-level thinking and reasoning are essential to learning and are a central component of most teaching interventions. However, formally assessing discussion quality 'at scale' is expensive and infeasible for most researchers. In this work, we experimented with various modern natural language processing (NLP) techniques to automatically generate rubric scores for individual dimensions of classroom text discussion quality. Specifically, we worked on a dataset of 90 classroom discussion transcripts consisting of over 18000 turns annotated with fine-grained Analyzing Teaching Moves (ATM) codes and focused on four Instructional Quality Assessment (IQA) rubrics. Despite the limited amount of data, our work shows encouraging results in some of the rubrics while suggesting that there is room for improvement in the others. We also found that certain NLP approaches work better for certain rubrics.