iAttention Transformer: An Inter-Sentence Attention Mechanism for Automated Grading

This study developed and evaluated transformer-based models enhanced with inter-sentence attention (iAttention) mechanisms to improve the automatic grading of student responses to open-ended questions. Traditional transformer models emphasize intra-sentence relationships and often fail to capture complex semantic alignments needed for accurate assessment. To overcome this limitation, three iAttention mechanisms, including iAttentionTF−IDF, iAttentionword and iAttentionHW were proposed to enhance the model’s capacity to align key ideas between students and reference answers. This helps improve the model’s ability to capture important semantic relationships between words in two sentences. Unlike previous approaches that rely solely on aggregated sentence embeddings, the proposed method introduces inter-sentence attention layers that explicitly model semantic correspondence between individual sentences. This enables finer-grained matching of key concepts, reasoning, and logical structure which are crucial for fair and reliable assessment. The models were evaluated on multiple benchmark datasets, including Semantic Textual Similarity (STS), SemEval-2013 Beetle, SciEntsBank, Mohler, and a composite of university-level educational datasets (U-datasets). Experimental results demonstrated that integrating iAttention consistently outperforms baseline models, achieving higher Pearson and Spearman Correlation scores on STS, Mohler, and U-datasets, as well as superior Macro-F1, Weighted-F1, and Accuracy on the Beetle and SciEntsBank datasets. This approach contributes to the development of scalable, consistent, and fair automated grading systems by narrowing the gap between machine evaluation and human judgment, ultimately leading to more accurate and efficient assessment practices.