{
  "id": 6923,
  "url": "https://arxiv.org/abs/2604.19754v1",
  "title": "Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom",
  "summary": "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",
  "authors": "Prudence Djagba, Kevin Haudek, Clare G. C. Franovic, Leonora Kaldaras",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-21T07:12:10.000Z",
  "fetched_at": "2026-07-14T16:32:50.147Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6923",
  "original_url": "https://arxiv.org/abs/2604.19754v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}