Existing work on generating hints in Intelligent Tutoring Systems (ITS)
focuses mostly on manual and non-personalized feedback. In this work, we
explore automatically generated questions as personalized feedback in an ITS.
Our personalized feedback can pinpoint correct and incorrect or missing phrases
in student answers as well as guide them towards correct answer by asking a
question in natural language. Our approach combines cause-effect analysis to
break down student answers using text similarity-based NLP Transformer models
to identify correct and incorrect or missing parts. We train a few-shot Neural
Question Generation and Question Re-ranking models to show questions addressing
components missing in the student answers which steers students towards the
correct answer. Our model vastly outperforms both simple and strong baselines
in terms of student learning gains by 45% and 23% respectively when tested in a
real dialogue-based ITS. Finally, we show that our personalized corrective
feedback system has the potential to improve Generative Question Answering
systems.

研究了如何使用自动生成的问题作为智能辅导系统中个性化反馈的一种方法，此方法利用了因果分析和自然语言处理转换器模型以识别学生答案中的正确和错误部分，并训练了几个模型，以促使学生朝向正确的答案，并最终显示该方法对学生的学习效果提升达到 45%，拥有潜力改善生成式问答系统。