Improving Academic Skills Assessment with NLP and Ensemble Learning

TL;DR

This paper presents an ensemble NLP approach combining multiple models to improve the accuracy and efficiency of academic skills assessment, addressing limitations of traditional methods.

Contribution

It introduces a novel ensemble framework integrating BERT, RoBERTa, BART, DeBERTa, and T5 with stacking techniques for enhanced assessment accuracy.

Findings

Significant improvement in assessment accuracy over traditional methods

Effective integration of multiple NLP models within an ensemble framework

Enhanced feedback timeliness and comprehensiveness

Abstract

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.