CAREER: Mapping and enhancing the acquisition of conceptual knowledge using behavior, neural signals, and natural language processing models

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。该项目还得到了EHR核心研究（ECR）项目的资助，并得到了SBE人类网络和数据科学（HNDS）项目的共同资助。达特茅斯大学的这个CAREER项目的目标是促进我们对学生如何通过在线课程视频学习STEM概念的理解，以改善在线教育。互联网骨干网的持续扩展和计算硬件的改进促进了视频流的改进，使视频能够更容易地下载和共享。这反过来又提出了一些紧迫的全国性问题。例如，什么是有效的课程或培训计划？教学的哪些方面可以优化或自动化？学习需求和目标如何以及为什么会因人而异？我们如何降低实现高质量教育的障碍？这个项目的重点是了解如何为学习者提供自动化的教学，是定制每个人的需求。它可能会对STEM概念的在线学习以及如何为不同社区的成员提供个性化服务产生重大影响。 此外，研究者自己的教学和指导将把机器学习，认知神经科学和教学设计结合在一起，为下一代科学家提供发展机会，这些科学家致力于表征和评估现实世界的教学和学习框架。 他将创建夏季研讨会，定期教程系列和一系列开放的在线课程。该项目旨在（1）建立一个跟踪个别学生概念学习和理解的计算框架;（2）测试大脑记录是否以及如何用于评估正在进行的概念学习和理解。该项目将收集学生从真实的在线教学视频中学习的数据，以告知，拟合和测试STEM领域（如天文学和计算机科学编程）的真实世界概念学习模型。通过要求参与者解决应用问题来测试概念知识，这些问题要求他们在培训期间提出的具体示例之外进行概括。应用基于文本嵌入的模型运行在这些数据上，研究人员将得出所教概念和学生所学内容的语义图。该项目还将涉及收集参与者的大型数据集，这些参与者在进行神经成像时参与了一系列课程视频。实验数据将用于构建每个参与者的每时每刻的概念知识和他们获得新知识的能力的动态估计。该项目将使用自然语言处理模型来量化概念以及它们之间的关系。这项工作将为以后的自动适应学习系统的研究和开发提供基础，该系统可以评估个人的概念知识并编辑在线教学材料，以便为个人量身定制。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The project is also funded from the EHR Core Research (ECR) program with co-funding from the Human Networks and Data Science (HNDS) program in SBE.The goal of this CAREER project at Dartmouth University is to advance our understanding of how students learn STEM concepts through online course videos in order to improve online education. Continued expansion of the internet backbone and improvements in computing hardware have facilitated improvements in video streaming, enabling videos to be more easily downloaded and shared. This, in turn, raises a number of questions of pressing national concern. For example, what makes for an effective course or training program? Which aspects of teaching might be optimized or automated? How and why do learning needs and goals vary across people? How might we lower barriers to achieving a high quality education? The focus of this project is to understand how to provide learners with automatized instruction that is customized to the needs of each individual. It could have a significant impact on the online learning of STEM concepts and how it can be individualized for members of different communities. Moreover, the investigator’s own teaching and mentoring will bring machine learning, cognitive neuroscience, and instructional design together in a way that will provide development opportunities for the next generation of scientists working on frameworks for characterizing and evaluating real-world teaching and learning. He will create summer workshops, a regular tutorial series, and a series of open online courses. The project aims to (1) to build a computational framework for tracking individual students’ conceptual learning and understanding; and (2) to test whether and how brain recordings can be used to estimate ongoing conceptual learning and understanding. The project will collect data of what students learn from real online instructional videos to inform, fit, and test models of real-world conceptual learning in such STEM domains as Astronomy and Computer Science programming. Conceptual knowledge is tested by asking participants to solve applied problems that require them to generalize beyond the specific examples presented during training. Applying text embedding-based models run on these data, the investigators will derive semantic maps both of the concepts taught and of what the students learn. The project will also involve collecting a large dataset from participants who engage with a sequence of course videos while undergoing neuroimaging. The experimental data will be used to construct dynamic estimates of each participant’s moment-by-moment conceptual knowledge and their ability to acquire new knowledge. The project will use natural language processing models to quantify concepts and how they relate. This work will provide a foundation for later research and development of automatic adaptive learning systems that can assess individual conceptual knowledge and edit online instructional material so that it is tailored to that individual.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.