Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion

Autor:	Tran, Nhat, Pierce, Benjamin, Litman, Diane, Correnti, Richard, Matsumura, Lindsay Clare
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: to be published in AIED 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.14918 Zobrazit plný text záznamu View this record from Arxiv