We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.

翻译：我们探索在智能辅导系统(ITS)中创建自动的、个性化的反馈。我们的目标是在学生的回答中找到正确和不正确的概念,以便取得更好的学生学习成绩。虽然存在提供个性化反馈的自动方法,但它们并没有明确地告知学生其回答中哪些概念正确或不正确。我们的方法是使用神经话语分解和分类技术将学生的回答分解。这种分解在参考解决方案和学生回答所涵盖的所有讨论单元中产生一个关联图。我们用这个推断的关联图结构和神经分类器将学生的回答与参考解决方案相匹配,并产生个性化反馈。虽然这一过程是完全自动化和数据驱动的,但产生的个性化反馈具有高度的关联性、域觉悟性并有效地针对每个学生的误解和知识差距。我们用基于对话的ITS测试我们的方法,并表明我们的方法在高质量的反馈和显著改善学生学习成果方面的结果。