Document Type

Article

Publication Date

2025

DOI

10.1186/s43031-025-00149-5

Publication Title

Disciplinary & Interdisciplinary Science Education Research

Volume

7

Issue

1

Pages

26 (1-22)

Abstract

The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students' contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the current teaching and learning of science. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students' responses from a cognitive perspective. Despite BERT's performance in downstream tasks, challenges may arise in domain-specific tasks, particularly in establishing knowledge connections between specialized and open domains. These challenges become pronounced in small-scale and imbalanced educational datasets, where the available information for fine-tuning is frequently inadequate to capture task-specific nuances and contextual details. The primary objective of the present study is to investigate the effectiveness of a pretrained language model, when integrated with an ontological framework aligned with a contextualized science assessment, in classifying students' expertise levels in scientific explanation. Our findings indicate that while pretrained language models, such as BERT, contribute to enhanced performance in language-related tasks within educational contexts, the incorporation of identifying domain-specific terms and extracting and substituting with their associated sibling terms in sentences through ontology-based systems can significantly improve classification model performance. Further, we qualitatively examined student responses and found that, as expected, the ontology framework identified and substituted key domain-specific terms in student responses that led to more accurate predictive scores. The study explores the practical implementation of ontology in assessment evaluation to facilitate formative assessment and formulate instructional strategies.

Rights

© 2025 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Data Availability

Article states: "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Code available at https://github.com/IvyWang845/Ontology-Use-For-Textual-Analysis."

Original Publication Citation

Wang, H., Haudek, K. C., Manzanares, A. D., Romulo, C. L., Royse, E. A., & Azzarello, C. B. (2025). Extending a pretrained language model (BERT) using an ontological perspective to classify students' scientific expertise level from written responses. Disciplinary & Interdisciplinary Science Education Research, 7(1), 1-22, Article 26. https://doi.org/10.1186/s43031-025-00149-5

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