用NLP与集成学习提升学术能力评估的准确性和效率
Improving Academic Skills Assessment with NLP and Ensemble Learning
- 融合BERT、RoBERTa等5个先进NLP模型，用堆叠法集成预测
- 在真实写作数据集上，评估准确率显著优于传统方法
- 适合教育科技研究者及自动化评分系统开发者
本研究针对学术能力评估中的关键挑战，利用自然语言处理（NLP）技术进步，改进对连贯性、句法和分析推理等核心认知与语言能力的评估。传统评估方法常难以及时提供全面反馈。本文将BERT、RoBERTa、BART、DeBERTa和T5等多个前沿NLP模型集成于堆叠式集成学习框架中，采用LightGBM和岭回归进行融合。通过精细化数据预处理、特征提取及伪标签学习优化模型性能。实验表明，该方法显著提升了评估的准确性与效率，超越传统手段，为教育技术领域提升核心学术素养提供了可靠新路径。
原文摘要 · Abstract (English)
This study addresses the critical challenges of assessing foundational academic skills by leveraging advancements in natural language processing (NLP). Traditional assessment methods often struggle to provide timely and comprehensive feedback on key cognitive and linguistic aspects, such as coherence, syntax, and analytical reasoning. Our approach integrates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5, within an ensemble learning framework. These models are combined through stacking techniques using LightGBM and Ridge regression to enhance predictive accuracy. The methodology involves detailed data preprocessing, feature extraction, and pseudo-label learning to optimize model performance. By incorporating sophisticated NLP techniques and ensemble learning, this study significantly improves the accuracy and efficiency of assessments, offering a robust solution that surpasses traditional methods and opens new avenues for educational technology research focused on enhancing core academic competencies.
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