What Differentiates Educational Literature? A Multimodal Fusion Approach of Transformers and Computational Linguistics

TL;DR

This paper introduces a multimodal AI approach combining transformers and linguistic features to evaluate and align educational texts with curriculum standards, enhancing decision-making in English literature education.

Contribution

It presents a novel fusion of transformer-based text classification with linguistic analysis, significantly improving accuracy in assessing text complexity for educational purposes.

Findings

Multimodal approach outperforms unimodal models in accuracy.

ELECTRA transformer with neural network achieves F1 score of 0.996.

Statistically significant improvements in validation metrics across models.

Abstract

The integration of new literature into the English curriculum remains a challenge since educators often lack scalable tools to rapidly evaluate readability and adapt texts for diverse classroom needs. This study proposes to address this gap through a multimodal approach that combines transformer-based text classification with linguistic feature analysis to align texts with UK Key Stages. Eight state-of-the-art Transformers were fine-tuned on segmented text data, with BERT achieving the highest unimodal F1 score of 0.75. In parallel, 500 deep neural network topologies were searched for the classification of linguistic characteristics, achieving an F1 score of 0.392. The fusion of these modalities shows a significant improvement, with every multimodal approach outperforming all unimodal models. In particular, the ELECTRA Transformer fused with the neural network achieved an F1 score of 0.996. Unimodal and multimodal approaches are shown to have statistically significant differences in all validation metrics (accuracy, precision, recall, F1 score) except for inference time. The proposed approach is finally encapsulated in a stakeholder-facing web application, providing non-technical stakeholder access to real-time insights on text complexity, reading difficulty, curriculum alignment, and recommendations for learning age range. The application empowers data-driven decision making and reduces manual workload by integrating AI-based recommendations into lesson planning for English literature.