Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
📰 ArXiv cs.AI
Learn to address class imbalance in AI scoring of scientific explanations using data augmentation and resampling strategies for transformer-based models
Action Steps
- Apply data augmentation techniques to balance class distribution in datasets
- Use resampling strategies such as oversampling the minority class or undersampling the majority class
- Implement transformer-based models for text classification tasks
- Evaluate the performance of models using metrics such as precision, recall, and F1-score
- Compare the effectiveness of different data augmentation and resampling strategies
Who Needs to Know This
Data scientists and AI engineers working on natural language processing tasks, particularly those involved in education technology, can benefit from this research to improve the accuracy of AI scoring systems
Key Insight
💡 Data augmentation and resampling strategies can improve the performance of transformer-based models in addressing class imbalance in AI scoring of scientific explanations
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Improve AI scoring of scientific explanations with data augmentation & resampling strategies #AIinEd #NLP
Key Takeaways
Learn to address class imbalance in AI scoring of scientific explanations using data augmentation and resampling strategies for transformer-based models
Full Article
Title: Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom
Abstract:
arXiv:2604.19754v1 Announce Type: new Abstract: 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
Abstract:
arXiv:2604.19754v1 Announce Type: new Abstract: 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
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