In this paper, we introduce "Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students with visual highlights. Unlike traditional systems that provide binary scores, "marking" identifies and categorizes segments of the student response as correct, incorrect, or irrelevant and detects omissions from gold answers. We introduce a new dataset meticulously curated by Subject Matter Experts specifically for this task. We frame "Marking" as an extension of the Natural Language Inference (NLI) task, which is extensively explored in the field of Natural Language Processing. The gold answer and the student response play the roles of premise and hypothesis in NLI, respectively. We subsequently train language models to identify entailment, contradiction, and neutrality from student response, akin to NLI, and with the added dimension of identifying omissions from gold answers. Our experimental setup involves the use of transformer models, specifically BERT and RoBERTa, and an intelligent training step using the e-SNLI dataset. We present extensive baseline results highlighting the complexity of the "Marking" task, which sets a clear trajectory for the upcoming study. Our work not only opens up new avenues for research in AI-powered educational assessment tools, but also provides a valuable benchmark for the AI in education community to engage with and improve upon in the future. The code and dataset can be found at https://github.com/luffycodes/marking.

通过对学生反馈进行深入分析并提供学生视觉亮点，本文引入了一种名为“Marking”的新型评分任务，以增强自动评分系统，与传统系统不同，通过将学生反应进行正确、错误或无关的分类，并检测与标准答案的遗漏，我们将之作为自然语言推理任务的扩展，通过训练语言模型识别学生响应的蕴含、矛盾和中性，同时识别标准答案的遗漏，我们使用BERT和RoBERTa等变压器模型，并结合e-SNLI数据集进行智能训练，在基准结果中展示了“Marking”任务的复杂性，为未来的研究设定了明确的发展方向，我们的工作不仅为AI驱动的教育评估工具的研究开辟了新的途径，还为AI教育社区提供了有价值的基准，以便今后的改进。

视觉分级与突出错误标记与标注缺失位