Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Automated scoring of students' scientific explanations offers the potential for immediate, accurate feedback, yet class imbalance in rubric categories particularly those capturing advanced reasoning remains a challenge. This study investigates augmentation strategies to improve transformer-based text classification of student responses to a physical science assessment based on an NGSS-aligned learning progression. The dataset consists of 1,466 high school responses scored on 11 binary-coded anal
Record details
Published: 21 March 2026
Source: arXiv
Category: Research
Topics: Children & education · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (21 March 2026), “Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom,” evidence record 6923, https://ethics.ai/record/6923 (originally published by arXiv).
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