Automated Answersheet Evaluation using BERT

Abstract

The research introduces an innovative approach to automating answersheet evaluation. The methodology combines the power of BERT (Bidirectional Encoder Representations from Transformers) and cosine similarity within the realm of natural language inference, allowing for a sophisticated analysis of student responses. BERT’s contextual embeddings provide a deep understanding of the semantic nuances present in answers, while cosine similarity serves as a robust metric for measuring the semantic alignment between student responses and ideal answers. Natural language inference enriches the model’s capacity to decipher inferential relationships, enabling a more nuanced eval- uation that goes beyond surface-level language comprehension. Augmented by natural language inference, our model adeptly captures inferential relationships, enhancing its comprehension of nuanced contextual nuances. The results underscore its potential to redefine automated grading paradigms, offering educators a powerful tool for efficient and unbiased assessments of student answers, thereby contributing to the advancement of educational technology and assessment methodologies.