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

这个SBIR第一阶段项目的更广泛/商业影响是一个拟议的辅导系统，有可能满足真正可扩展的个人辅导的需要，始终等于一个高于平均水平的导师的表现。 由合格的个人导师进行一对一的指导是最佳的教学形式。 然而，训练有素的私人辅导员是一种有限的资源，特别是在贫困地区以及科学、技术、工程和数学方面，而且绝大多数学区和家长都没有预算。 然而，与人类导师不同的是，这里提出的系统可以在校外的任何时候使用;它可以随意停止和恢复，可以在家里使用，并且对经济，性别和其他社会线索视而不见。 该系统将提供给价值340亿美元的课后教育、考试准备和电子学习市场，最初的切入点是为高中生提供大学先修课程（AP）考试准备服务。适合拟议系统的学习模式的科目占每年100万次考试，导致每年4亿美元的初始总可用市场。这一滩头阵地市场将扩大到包括高中和高等教育的全年辅导。该项目将开发一个计算机化辅导平台，通过监测学生的大脑活动，实时调整其教学策略。 这一创新将弥补当前基于计算机的自适应训练技术的不足，这些技术依赖于广义的学习者状态模型以及对学生参与度和认知工作量的推断，因此缺乏人类导师的一些关键能力。 该平台将结合联合收割机的优势，智能辅导系统模仿专家人类导师的行为，并具有创新的脚手架模式与新开发的可穿戴大脑监测技术。 第一阶段的目标是评估基于EEG的认知工作量和参与学生在课堂环境中执行学习任务的量表，评估量表的定义难度的学习任务，并定义使用量表修改教学内容的方法。 将在一所伙伴高中的学生中进行试验，其预期结果是测量具有高分类准确性的认知量表。 将开发一个结构框架的认知状态计将如何与辅导系统的接口和优化其教学策略，并定义原型系统的用户需求。

This SBIR Phase I project's broader/commercial impact is a proposed tutoring system with the potential to meet the need for truly scalable personal tutoring by consistently equaling the performance of an above-average tutor. One-on-one instruction by qualified personal tutors is the optimum form of teaching. Well-trained personal tutors, however, are a limited resource, particularly in disadvantaged areas and for science, technology, engineering and mathematics, and are outside the budget of the vast majority of school districts and parents. However, unlike human tutors, the system proposed here can be available at any time outside school; it can be stopped and resumed at will, can be utilized at home, and is blind to economic, gender and other social cues. The system will be offered to the $34 billion after-school education, test-prep and eLearning market, with an initial entry point in Advanced Placement (AP) exam preparation services to high school students. Subjects suitable for the learning paradigm of the proposed system account for 1 million annual exams, resulting in to an initial Total Available Market of $400 million annually. This beachhead market will be expanded to include general year-round tutoring for both high school and higher education.The project will develop a computerized tutoring platform that adapts its teaching strategy to students in real-time by monitoring their brain activity. This innovation will meet shortfalls in current computer-based adaptive training technologies, which rely on generalized learner state models and inferences of student engagement and cognitive workload, and thereby lack some of the key abilities of human tutors. This platform will combine the strengths of an Intelligent Tutoring System modeled after expert human tutor behavior and has an innovative scaffolding mode with newly-developed wearable brain monitoring technology. The Phase I objectives are to evaluate EEG-based gauges of cognitive workload and engagement on students executing learning tasks in a classroom environment, evaluate the gauges in response to learning tasks of defined difficulty, and define methods of using the gauges to modify instructional content. A trial will be carried out on students at a partner high school, the anticipated outcome of which is the measurement of cognitive gauges with high classification accuracy. A structural framework of how the cognitive state gauges will interface with the tutoring system and optimize its instructional strategy will be developed, and user requirements for the prototype system will be defined.