SBIR Phase I: Education Revolution Socrates Learning Engine

SBIR第一阶段项目将资助开发和发布一种使用大数据的新方法，以最大限度地提高教育成果和每个孩子的潜力。该项目将为每个学生创建个性化的学习路径，根据他们在数千个教育主题中的能力进行动态调整。无论年龄或年级，发动机都会向上调整以发现他们的最大潜力，并在他们挣扎时向下调整。结合游戏化元素的简单而吸引人的游戏将让孩子们想要不断地玩，不断地学习。该项目将为希望提高孩子素质的父母提供一个工具。以及那些喜欢在家上学的孩子。它还将成为一个强大的教师助手，能够监控课堂上每个孩子的进步，这样老师就可以更好地监控和管理学生群体的进步。家长和教师门户网站将把课堂和家庭学习经验联系起来，让家长和教师都能看到每个孩子的表现，以及他们需要在哪里进行干预和帮助。随着时间的推移，该项目将测试和部署新的学习方法。该产品的潜在商业市场包括家长、在家上学的孩子和教师。最初的目标市场是幼儿园到八年级。该项目将改善这些关键年龄段的教育成果。该项目使用了一项正在申请专利的技术，该技术结合了一组概率引擎，根据每个学生的个人能力确定适合他们的内容。它不会根据学生的年龄或年级来决定内容，也不会假设学生能做什么或能做什么。t。相反，它允许学生设定自己的极限，并使用大数据来预测和测试最佳学习路径。学习引擎可以处理任何类型的教育内容，并将这些内容动态地传送到任何类型的游戏中。该项目可以通过改变学生群体中的传统学习路径来衡量学习影响。例如，它将揭示是否应该更早地向所有儿童或儿童的子集介绍分数。与跳级或留级的选择相反，一个表现好的（或差的）学生可以和他们的同龄人在同一个班级，仍然有独特的内容挑战他们，最大限度地发挥他们的天赋。学习引擎将使用商业智能技术来识别每个孩子的问题领域，并建议练习什么和练习技巧。项目团队之前已经获得了游戏行业的技术专利，并且有信心获得该技术的实用专利以保护知识产权。

This SBIR Phase I project will fund development and release of a new way of using big data to maximize educational outcomes and the potential of each individual child. The project will create individualized learning paths for every student that dynamically adjusts based on their abilities across thousands of educational topics. The engine adjusts up to find their maximum potential regardless of age or grade, and adjusts down when they are struggling. The simple, yet engaging, games combined with gamification elements will keep the child wanting to play over and over, constantly learning as they go. The project will provide a tool to parents looking to enhance their child?s education as well as to those that prefer to homeschool. It will also be a powerful teacher's assistant, able to monitor the progress of each child in the classroom so the teacher can better monitor and manage progress of their student base. The Parent and Teacher Portals will connect the classroom and home learning experience and allow both parents and teachers to see how each child is performing and where they need to intervene to help. Over time, the project will test and deploy new ways to learn. The potential commercial market for this product includes parents, homeschoolers, and teachers. The initial target market is kindergarten to 8th grade. The project will improve education outcomes at these crucial ages. The project uses patent-pending technology that combines a set of probabilistic engines to determine the right content for each student based on their individual ability. It does not determine content based on their age or grade and makes no assumptions about what the student can or can?t do. Instead, it allows the student to set their own limits and use Big Data to predict and test optimal learning paths. The learning engine can work with any category of educational content and deliver that content dynamically to any type of game. The project can measure the learning impact by making changes to the traditional learning paths within clusters of students. It will uncover if, for example, fractions should be introduced earlier to all children, or subsets of children. As opposed to the option of skipping a grade or being held back, a student doing well (or poorly) could be in the same class with their peers and still have unique content that challenges them and maximizes their natural abilities. The learning engine will use business intelligence techniques to identify problem areas for each child and recommend what to practice and practice techniques. The project team has previously received patents on technology in the gaming industry, and is confident in the ability to receive a utility patent for this technology for intellectual property protection.