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，有可能通过始终如一地与高于平均水平的辅导教师的表现持平来满足对真正可扩展的个人辅导的需求。由合格的私人导师进行一对一指导是最佳的教学形式。然而，训练有素的私人教师是一种有限的资源，特别是在贫困地区以及科学、技术、工程和数学方面，而且超出了绝大多数学区和家长的预算。然而，与人类导师不同的是，这里提出的系统可以在学校外随时使用；它可以随意停止和恢复，可以在家里使用，并且对经济、性别等社会线索视而不见。该系统将提供给价值340亿美元的课后教育、备考和电子学习市场，最初的切入点是为高中生提供高级分级(AP)考试准备服务。适合拟议系统学习范式的科目占年度考试的100万门，导致初步的可用市场总额为每年4亿美元。这个滩头市场将扩大到包括普通高中和高等教育的全年辅导。该项目将开发一个计算机化的辅导平台，通过监控学生的大脑活动来实时调整其教学策略。这一创新将弥补目前基于计算机的自适应培训技术的不足，这些技术依赖于通用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。该平台将结合模仿专家人类导师行为的智能辅导系统的优势，并具有创新的脚手架模式和最新开发的可穿戴式大脑监控技术。第一阶段的目标是评估基于脑电的认知工作量和对学生在课堂环境中执行学习任务的参与度的测量，根据定义的难度的学习任务对测量进行评估，并定义使用测量来修改教学内容的方法。将在一所合作高中的学生身上进行一项试验，其预期结果是测量具有高分类准确性的认知量表。将制定认知状态量表如何与辅导系统对接并优化其教学策略的结构框架，并定义用户对原型系统的要求。

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.