DIP: Exploiting Longitudinal Electroencephalogram (EEG) Input in a Reading Tutor

DIP：利用阅读导师的纵向脑电图 (EEG) 输入

• 批准号: 1124240
• 负责人: David Mostow
• 金额: $ 135万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1124240&HistoricalAwards=false
• 关键词: DIP; Exploiting; Longitudinal; Electroencephalogram; EEG

Automated (and human) tutors are limited in their ability to infer what is going on in students' heads based on their observable behavior. The proposed work addresses this limitation by investigating how EEG input from a commercially-available device can be used as evidence about students' mental states. In particular, the project focuses on adding EEG-enhanced feedback to Project LISTEN's Reading Tutor, an intelligent tutoring system that helps children learn to read. The project seeks to answer two questions: (1) How can we use EEG to detect mental states that predict, indicate, or reflect student learning? (2) How can we use such detection to improve student learning? Analysis to answer these questions and to enhance the capabilities of the Reading Tutor draws on existing tools to explore annotate, and mine EEG data logged by the Reading Tutor. The research aims to tell us more about how to use EEG to identify mental states that predict learning and to use machine learning to make an intelligent tutoring system better, and it may also add to what is known about sources of reading difficulties. Expected technological contributions of this work include advances in relating EEG data to children's behavior, cognition, engagement, and learning and advances in elucidating how intelligent tutors can robustly exploit noisy EEG input to better assist learning. The technological innovation in this project is particularly important for those children who need extra help with sounding out, word recognition, and/or making simple inferences needed for understanding.

自动化（和人类）导师根据学生可观察到的行为来推断学生头脑中发生的事情的能力有限。拟议的工作解决了这一限制，通过调查如何从商业上可用的设备的EEG输入可以作为证据，对学生的精神状态。特别是，该项目侧重于将EEG增强反馈添加到Project LISTEN的阅读导师中，这是一个帮助儿童学习阅读的智能辅导系统。该项目旨在回答两个问题：（1）我们如何使用EEG来检测预测，指示或反映学生学习的心理状态？(2)我们如何使用这种检测来改善学生的学习？回答这些问题并增强阅读导师能力的分析利用现有工具来探索、注释和挖掘由阅读导师记录的EEG数据。这项研究旨在告诉我们更多关于如何使用EEG来识别预测学习的精神状态，以及如何使用机器学习来使智能辅导系统更好，它也可能增加对阅读困难来源的了解。这项工作的预期技术贡献包括将EEG数据与儿童的行为，认知，参与和学习相关的进展，以及阐明智能导师如何稳健地利用嘈杂的EEG输入来更好地辅助学习的进展。这个项目的技术创新对于那些需要额外帮助的孩子来说尤其重要，他们需要发声，单词识别和/或进行理解所需的简单推理。