PFI-TT: Using Artificial Intelligence to Identify Additional Educational Resources Based on What was Discussed in Class

这个创新技术转化伙伴关系(PFI-TT)项目的更广泛的影响/商业潜力是创建本科生教育背景链接(CLUE)服务，该服务分析课堂录音的转录并自动识别学生学习生态系统中的相关资源。该系统将允许学生搜索课堂视频，以找出概念被讨论的时间，指示他们在课堂上感到困惑的时刻，并随后接收关于令人困惑的主题的额外信息。通过自动识别课堂上讨论的关键概念，这个项目将使教育平台能够向学习者提供个性化的内容，使他们能够在每节课上展示材料。教师将收到学生认为对每个主题有价值的资源的反馈。线索服务的目的是通过提供与其他教育平台上的资源的背景链接来帮助教育资源提供者。大学首席信息官将发现有价值的方式来整合他们支持的大量学习资源。拟议的项目建立在以前由NSF资助的项目基础上，需要自然语言处理和教育技术设计方面的技术专长。线索服务将使用计算机生成的课堂记录来识别课堂上讨论的关键术语和短语。分析的关键术语和相应的时间戳的组合将允许在课堂捕获的时刻和其他教育资源之间建立上下文联系。设想的原型将是一个可订阅的应用程序编程接口(API)，它将允许教育服务之间的上下文联系。这个项目的技术挑战包括如何最佳地设计无监督机器学习，以便识别课堂上的关键字和/或主题以及课堂上的最佳时间戳(S)来表示这些关键字或主题，以及如何提供可以与任何视频传输系统一起使用的资源，以便学生可以在流媒体或录制课程期间表达他们想要的额外信息。最初，这项研究的结果将通过工具提供给参与的教师或他们的助手，使他们能够评估推荐资源对学生的潜在价值。这一反馈将为线索服务在资源选择方面的改进提供信息。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI- TT) project is to create the Contextual Linkaging for Undergraduate Education (CLUE) Service that analyzes transcriptions from class recordings and automatically identifies related resources in a student’s learning ecosystem. This system will allow students to search class videos to find when a concept was discussed, indicate moments during a class when they are confused, and subsequently receive additional information on the confusing topics. By automatically identifying when key concepts were discussed during class, this project will enable educational platforms to deliver content to the learner personalized to the material presented during each class session. Instructors will receive feedback on which resources students find valuable for each topic. The CLUE Service will be designed to help educational resource providers by providing contextual linkages to resources in other educational platforms. University Chief Information Officers will find value as a way to contextually integrate the numerous learning resources they support.The proposed project builds on former NSF-funded projects and requires technical expertise in natural language processing and design of educational technologies. The CLUE Service will use computer-generated transcriptions from class captures to identify key terms and phrases discussed during class sessions. The combination of analyzed key terms and corresponding timestamps will allow contextual linkages to be created between moments in class captures and other educational resources. The envisioned prototype will be a subscribable Application Programming Interface (API) that will allow contextual linkages between educational services. Technical challenges for this project include how to best design unsupervised machine learning to either identify keywords and/or topics in a class session along with the best timestamp(s) in class to represent those keywords or topics and how to provide a resource that can be used with any video delivery system so the student can make known when they would like additional information during a streaming or recorded class session. Initially, the results of this research will be made available to participating instructors or their assistants with tools that will allow them to assess the potential value of the recommended resources to their students. This feedback will inform improvements in resource selection for the CLUE service.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.