论文标题
自动化的个性化反馈可以改善智能辅导系统中的学习收益
Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
论文作者
论文摘要
我们调查了大规模智能辅导系统（ITS）中自动化，数据驱动的个性化反馈是如何改善学生学习成果的。我们提出了一种机器学习方法来产生个性化的反馈，该反馈将学生的个人需求考虑在内。我们利用最先进的机器学习和自然语言处理技术为学生提供个性化的提示，基于Wikipedia的解释和数学提示。我们的模型用于Korbit，这是一种基于大规模的对话，与成千上万的学生在2019年启动，我们证明了个性化的反馈会导致学生学习成果以及对反馈的主观评估的可观改善。
We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
