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

这个SBIR第二阶段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。大多数学生，特别是贫困地区的学生和科学、技术、工程和数学(STEM)学科的学生，获得教师或合格导师的个性化指导的机会有限。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。这个项目将通过使辅导系统能够确定学生思考的努力程度和他们的注意力水平来应对这一挑战。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被用来指导教程主题的选择、内容的难度和互动的程度。该项目可通过增强计算机辅导平台的适应性和扩大教师和家长可获得的有关学生表现的信息的深度，导致教育技术的重大进步。适应性计算机教育现在是一个快速增长的市场，本项目开发的技术旨在为企业提供独特的能力来改进他们的产品，满足对真正可扩展的个性化辅导的需求。本项目的独特创新之处在于通过调整教学策略以适应学生的认知状态，创建一个与学生闭合的辅导平台。为了克服当前自适应辅导系统的局限性，该产品将测量学生的认知工作量和参与度，并使用这些信息以闭环方式调整教学策略的各个方面，如选题、内容难度和互动性。该项目的目标是修改当前的脑电波测量系统，以供儿童和年轻人使用，扩展相关的计算算法并验证它们符合行业标准，并评估将基于大脑的度量添加到作为自适应技术核心的学生模型的效果。为了实现这些目标，我们将进行研究和开发，以推动大脑活动监测技术的进步，并与6至12年级的学生进行研究，这些学生将在记录大脑活动的同时接受数学、生物和历史等科目的辅导。结果将被用来确定使用大脑活动数据来提高基于计算机的导师的适应性的效果，并扩大教育数据分析平台可用信息的深度。

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.