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ERIC Number: EJ1469454
Record Type: Journal
Publication Date: 2025
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: EISSN-2469-9896
Available Date: 0000-00-00
Comparing Large Language Models for Supervised Analysis of Students' Lab Notes
Physical Review Physics Education Research, v21 n1 Article 010128 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in students' typed lab notes through sentence-level labeling. Specifically, we use training data to fine-tune two different LLMs, BERT and LLaMA, and compare the performance of these models to both a traditional bag-of-words approach and a few-shot LLM (without fine-tuning). We evaluate the models based on their resource use, performance metrics, and research outcomes when identifying skills in lab notes. We find that higher-resource models often, but not necessarily, perform better than lower-resource models. We also find that all models report similar trends in research outcomes, although the absolute values of the estimated measurements are not always within uncertainties of each other. We use the results to discuss relevant considerations for education researchers seeking to select a model type for use as a classifier.
American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: https://journals.aps.org/prper/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Authoring Institution: N/A
Identifiers - Location: New York
Grant or Contract Numbers: N000142312729
Author Affiliations: N/A