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.

翻译：本研究针对评估基础学术能力的关键挑战，利用自然语言处理（NLP）领域的最新进展提出解决方案。传统评估方法往往难以及时、全面地反馈关键认知与语言能力，如连贯性、句法结构及分析推理。我们的方法将多种前沿NLP模型（包括BERT、RoBERTa、BART、DeBERTa和T5）集成于集成学习框架中。通过采用LightGBM与Ridge回归的堆叠技术融合这些模型，有效提升了预测精度。该方法涵盖详细的数据预处理、特征提取及伪标签学习以优化模型性能。通过整合先进的NLP技术与集成学习，本研究显著提高了评估的准确性与效率，提供了一种超越传统方法的稳健解决方案，为专注于提升核心学术能力的教育技术研究开辟了新途径。