Improving Academic Skills Assessment with NLP and Ensemble Learning

This paper develops an ensemble learning approach combining multiple NLP models (BERT, RoBERTa, BART, DeBERTa, T5) to automatically assess English Language Learners' essays in grades 8-12 across six linguistic dimensions (cohesion, syntax, vocabulary, phraseology, grammar, conventions). The system uses stacking techniques with LightGBM and Ridge regression to provide automated scoring and feedback on student writing.

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 co-herence, syntax, and analytical reasoning. Our approach inte-grates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5, within an ensemble learning framework. These models

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