论文标题
基于茎的大规模,开放域的混合面对话
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
论文作者
论文摘要
我们提出了Korbit,这是一种大型,开放域,混合接口,基于对话的智能辅导系统(ITS)。 Korbit使用机器学习,自然语言处理和强化学习来在线提供交互式,个性化的学习。 Korbit旨在通过自动化,标准化和简化内容创建过程来轻松地扩展到数千个受试者。与其他人不同,老师可以在几个小时内为Korbit开发新的学习模块。为了促进在STEM主题的广泛性方面学习,Korbit使用了混合界面,其中包括视频,基于交互式对话的练习,提问,概念图,数学练习和游戏化元素。 Korbit的建造是通过利用最先进的基于云的微服务体系结构来扩展数百万学生。科比特(Korbit)在2019年推出了机器学习的第一门课程,从那时起,有7,000多名学生就读了。尽管Korbit被设计为开放域且可扩展的,但与现实世界中的学生进行了A/B测试实验,表明与典型的在线课程相比,学生的学习成果和学生动机都得到了显着提高。
We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a widerange of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 on machine learning, and since then over 7,000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.