选择你自己的问题：在学习路径构建中鼓励自我个性化
Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
摘要：
学习路径推荐是适应性学习的核心，是交互式教育系统（IES）的教育范式，它根据学生的学习活动历史提供个性化的学习体验。在典型的现有IES中，学生必须完全消费推荐的学习项目才能获得新的推荐。此工作流有几个限制。例如，学生没有机会就IES选择的学习项目提供反馈。此外，做出选择的机制对学生来说是不透明的，限制了学生跟踪学习的能力。为此，我们介绍火箭，一个Tinder般的用户界面的一般类的IES。Rocket提供了人工智能（AI）提取的学习材料特征的可视化表示，允许学生快速决定材料是否满足他们的需求。学生可以选择参与材料，并通过滑动或点击接收新的建议。Rocket为IES用户界面提供了以下潜在的改进：首先，Rocket通过向学生展示决策过程中使用的有意义的AI提取特征的可视化摘要，增强了IES建议的可解释性。其次，Rocket通过利用学生对自身能力和需求的了解，实现学习体验的自我个性化。最后，Rocket为学生提供关于他们学习路径的细粒度信息，为他们提供评估自己技能和跟踪学习进度的途径。我们提出了火箭的源代码，在其中我们强调的独立性和可扩展性的每个组件，并使其公开可用于所有目的。
英文摘要：
Learning Path Recommendation is the heart of adaptive learning, the educational paradigm of an Interactive Educational System (IES) providing a personalized learning experience based on the student's history of learning activities. In typical existing IESs, the student must fully consume a recommended learning item to be provided a new recommendation. This workflow comes with several limitations. For example, there is no opportunity for the student to give feedback on the choice of learning items made by the IES. Furthermore, the mechanism by which the choice is made is opaque to the student, limiting the student's ability to track their learning. To this end, we introduce Rocket, a Tinder-like User Interface for a general class of IESs. Rocket provides a visual representation of Artificial Intelligence (AI)-extracted features of learning materials, allowing the student to quickly decide whether the material meets their needs. The student can choose between engaging with the material and receiving a new recommendation by swiping or tapping. Rocket offers the following potential improvements for IES User Interfaces: First, Rocket enhances the explainability of IES recommendations by showing students a visual summary of the meaningful AI-extracted features used in the decision-making process. Second, Rocket enables self-personalization of the learning experience by leveraging the students' knowledge of their own abilities and needs. Finally, Rocket provides students with fine-grained information on their learning path, giving them an avenue to assess their own skills and track their learning progress. We present the source code of Rocket, in which we emphasize the independence and extensibility of each component, and make it publicly available for all purposes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
