Automatic Scoring for Elementary Mathematics Solutions Based on Bi-LSTM and Attention Mechanism

Automated scoring for mathematical subjective responses remains challenging due to the inherent complexity of integrating symbolic notations, procedural reasoning, and natural language explanations. Traditional NLP approaches fail to capture domain-specific mathematical semantics and logical dependencies between problem-solving steps. This study proposes a novel neural architecture named BiLSTM-AM that synergizes Bi-LSTM with hierarchical attention mechanisms to tackle pivotal challenges in the automated scoring of mathematical subjective responses. The architecture effectively models the conjunction of formulaic expressions and textual narratives through a dual-channel embedding strategy, dynamically allocates weights to pivotal procedural elements using step-level attention, and automates the alignment of student-generated solutions with knowledge graphs constructed from expert solutions. Evaluated on the dataset of 1120 elementary mathematics solutions, this paper achieves 90.52 scoring accuracy, outperforming state-of-the-art baselines by 8.17 . The novelty of this research is underscored by its interpretable attention mechanisms, which offer quantitative means to track the propagation of errors throughout the steps of mathematical solutions. This study contributes to the field of AI-enhanced educational evaluation by introducing a scalable and curriculum-sensitive framework designed for the assessment of open-ended mathematics problems.