The increasing volume of student assessments, particularly open-ended responses, presents a significant challenge for educators in ensuring grading accuracy, consistency, and efficiency. This paper presents a structured dataset designed for the development and evaluation of automated grading systems in higher education. The primary objective is to create a high-quality dataset that facilitates the development and evaluation of natural language processing (NLP) models for automated grading. The dataset comprises student responses to open-ended questions from the Management Information Systems (MIS221) and Project Management (MIS415) courses at Covenant University, collected during the 2022/2023 academic session. The responses were originally handwritten, scanned, and transcribed into Word documents. Each response is paired with corresponding scores assigned by human graders, following a detailed marking guide. To assess the dataset’s potential for automated grading applications, several machine learning and transformer-based models were tested, including TF-IDF with Linear Regression, TF-IDF with Cosine Similarity, BERT, SBERT, RoBERTa, and Longformer. The experimental results demonstrate that transformer-based models outperform traditional methods, with Longformer achieving the highest Spearman’s Correlation of 0.77 and the lowest Mean Squared Error (MSE) of 0.04, indicating a strong alignment between model predictions and human grading. The findings highlight the effectiveness of deep learning models in capturing the semantic and contextual meaning of both student responses and marking guides, making it possible to develop more scalable and reliable automated grading solutions. This dataset offers valuable insights into student performance and serves as a foundational resource for integrating educational technology into automated assessment systems. Future work will focus on enhancing grading consistency and expanding the dataset for broader academic applications.

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 ${iAttention}_{TF-IDF}$, ${iAttention}_{word}$ and ${iAttention}_{HW}$ 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.