NCS-FO: Integrating Non-Invasive Neuroimaging and Educational Data Mining to Improve Understanding of Robust Learning Processes

从小学数学游戏到职场培训，基于计算机的学习应用正变得越来越普遍。有了这些程序，越来越有可能使用生成的数据，如正确和不正确的解决问题的反应，开发测试学生知识和个性化教学的方法，以满足学生的需求。学生响应的日志可以捕获答案，但它们无法捕获有关学生与软件交互之间暂停期间发生的事情的关键信息。该项目由亚利桑那州立大学和伍斯特理工学院的一组研究人员领导，将探索使用轻量级大脑传感器测量大脑活动以及学生日志数据，以了解学习期间重要的心理活动。本研究将探讨发展性数学学习在大学和社区大学的学生使用ASSISTments智能辅导系统。使用大脑成像，项目团队将检查学生是否在学习任务暂停期间深入思考问题或走神，并使用组合日志和大脑数据来预测学习结果。这项工作将为结合神经成像，机器学习和个性化学习环境的新方法奠定基础。通过更好地了解在辅导系统使用暂停期间学习何时以及如何发生，学习技术研究人员和开发人员将能够在辅导系统中创建自适应干预措施，这些干预措施更好地个性化以满足个人需求。该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，该项目是由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）和社会，行为和经济科学（SBE）董事会共同支持的多学科计划。该项目有三个目标：1）整合多个数据流以创建跨学科语料库; 2）检测日志数据暂停期间认知状态的实时变化;以及3）从基于大脑和基于日志的认知状态推断预测学习结果。为了实现这些目标，该团队将使用功能性近红外光谱神经成像技术收集大脑数据，并从与规则学习和思维漫游相关的受控、充分理解的任务以及真实学习任务中收集行为数据。涉及大脑活动记录的认知神经科学研究传统上需要具有高度约束的刺激，时间和任务要求的范式，而在复杂的现实世界环境中的研究，如辅导系统，很少与这些范式保持一致。在认知任务期间的大脑活动的特征将被用来对真实学习任务期间的学生认知进行推断。此外，大脑特征将与日志数据特征相结合，以创建机器学习模型，这些模型可以准确预测学生强大的学习成果，并在学生使用交互式学习环境后使用后测进行评估。该项目对STEM学习的贡献将包括更好地理解学生如何在数字学习环境中构建知识以应对教学事件，构建更好的预测模型，以预测学生何时从个性化学习环境中学习，以及学习过程与停顿的长度和上下文之间的映射。该项目还将有助于理解如何结合联合收割机分析神经影像数据和日志数据。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

From elementary school math games to workplace training, computer-based learning applications are becoming more widespread. With these programs, it becomes increasingly possible to use the data generated, such as correct and incorrect problem-solving responses, to develop ways to test for student knowledge and to personalize instruction to student needs. The logs of student responses can capture answers, but they fail to capture critical information about what is happening during pauses between student interactions with the software. This project, led by a team of researchers at Arizona State University and Worcester Polytechnic Institute, will explore the use of measurements of brain activity from lightweight brain sensors alongside student log data to understand important mental activities during learning. The study will examine developmental math learning in college and community college students using the ASSISTments intelligent tutoring system. Using brain imaging, the project team will examine whether students are thinking deeply about the problem or mind-wandering during pauses in the learning tasks and use the combined log and brain data to make predictions about learning outcomes. This work will build a foundation for new methods of combining neuroimaging, machine learning, and personalized learning environments. With a better understanding of when and how learning occurs during pauses in tutoring system use, learning technology researchers and developers will be able to create adaptive interventions within tutoring systems that are better personalized to the needs of the individual. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE).This project has of three goals: 1) Integrating multiple data streams for the creation of an interdisciplinary corpus; 2) Detecting real-time changes in cognitive states during pauses in log data; and 3) Predicting learning outcomes from brain-based and log-based inferences of cognitive states. In addressing these goals, the team will collect brain data, using functional near-infrared spectroscopy neuroimaging, and behavioral data from controlled, well-understood tasks related to rule learning and mind wandering and from authentic learning tasks. Cognitive neuroscience research involving recordings of brain activity traditionally requires paradigms with highly constrained stimuli, timing, and task requirements, whereas research in complex real-world environments such as tutoring systems rarely align with these paradigms. Features of the brain activity during the cognitive tasks will be used to make inferences about student cognition during authentic learning tasks. In addition, brain features will be combined with log data features to create machine learning models that make accurate predictions of student robust learning outcomes, to be assessed using a posttest given after students use the interactive learning environment. Contributions of this project to STEM learning will include improved understanding of how students build knowledge in response to instructional events within digital learning environments, the construction of better predictive models of when students learn from the use of personalized learning environments, and a mapping between learning processes and the length and context of pauses. This project will also contribute to understandings of how to combine analyses of neuroimaging data and log data.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.