SBIR Phase II: Intelligent tutoring system with EEG-based instructional strategy optimization

SBIR第二期：基于脑电图优化教学策略的智能辅导系统

• 批准号: 1556096
• 负责人: Neil McDonald
• 金额: $ 74.67万
• 依托单位: Quantum Applied Science & Research, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-15 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1556096&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Intelligent; tutoring

This SBIR Phase II project will develop a computer-based tutoring platform that adapts its teaching strategy to each student by monitoring their brain activity. Access to individualized instruction by teachers or qualified tutors is limited for a vast number of students, particularly in disadvantaged areas and for science, technology, engineering and mathematics (STEM) subjects. Computer-based teaching technology could meet this demand, but currently relies on indirect inferences of a student's state, and therefore lacks some of the key abilities of human teachers to assess a student during a lesson. This project will address this challenge by enabling the tutoring system to determine how hard students are thinking and their level of focus. This will be accomplished by measuring a student's brain electrical activity using a newly-developed headset designed specifically for school-aged children. Information derived from brain activity patterns will be used to guide tutorial topic selection, difficulty of the content and the level of interactivity. This project could lead to a significant advance in education technology by enhancing the adaptability of computer-based tutoring platforms and expanding the depth of information on student performance available to teachers and parents. Adaptive computer-based education is now a rapidly growing market, and the technology developed in this project aims to provide companies with a unique capability to improve their products and to meet the need for truly scalable individualized tutoring.The unique innovation of this project is to create a tutoring platform that closes the loop with the student by adapting its teaching strategy to the student?s cognitive state. To overcome the limitations of current adaptive tutoring systems, this product will measure the student's cognitive workload and engagement, and use this information to adapt aspects of the instructional strategy such as topic selection, content difficulty and interactivity in a closed-loop fashion. The goals of this project are to modify current brain-wave measurement systems for use by children and young adults, extend associated computational algorithms and validate them to an industry standard, and evaluate the effect of adding brain-based metrics to student models that are at the core of adaptive technologies. To meet these objectives, we will perform research and development to advance technologies for brain-activity monitoring, and conduct studies with students in grades 6 through 12 who will be tutored in subjects such as math, biology and history while their brain activity is recorded. The results will be used to determine the effects of using brain activity data to improve the adaptability of computer-based tutors and expand the depth of information available to educational data analytics platforms.

SBIR第二期项目将开发一个基于计算机的辅导平台，通过监测每个学生的大脑活动来调整其教学策略。对于大量学生来说，获得教师或合格导师的个性化指导的机会有限，特别是在贫困地区和科学、技术、工程和数学（STEM）学科。基于计算机的教学技术可以满足这一需求，但目前依赖于对学生状态的间接推断，因此缺乏人类教师在课堂上评估学生的一些关键能力。该项目将通过使辅导系统能够确定学生的思考程度和专注程度来解决这一挑战。这将通过使用专门为学龄儿童设计的新开发的耳机来测量学生的脑电活动来实现。来自大脑活动模式的信息将用于指导教程的选题、内容的难度和互动性水平。该项目可以通过增强计算机辅导平台的适应性，扩大教师和家长可获得的学生表现信息的深度，从而导致教育技术的重大进步。自适应计算机教育现在是一个快速增长的市场，在这个项目中开发的技术旨在为公司提供一种独特的能力来改进他们的产品，并满足真正可扩展的个性化辅导的需求。该项目的独特创新之处在于，通过调整教学策略，创建一个与学生形成闭环的辅导平台。认知状态。为了克服当前适应性辅导系统的局限性，该产品将测量学生的认知工作量和参与度，并利用这些信息以闭环方式调整教学策略的各个方面，如选题、内容难度和交互性。该项目的目标是修改当前的脑波测量系统，以供儿童和年轻人使用，扩展相关的计算算法并将其验证为行业标准，并评估将基于大脑的指标添加到自适应技术核心的学生模型中的效果。为了实现这些目标，我们将进行研究和开发先进的大脑活动监测技术，并对6至12年级的学生进行研究，这些学生将在数学、生物和历史等科目上接受辅导，同时记录他们的大脑活动。研究结果将用于确定使用大脑活动数据的效果，以提高计算机教师的适应性，并扩大教育数据分析平台可用的信息深度。