SBIR Phase I: Measure What We Treasure: Developing a Structural Cognitive Analytics and Assessment (SCAA) Technology

这个SBIR第一阶段项目将解决我们目前无法衡量信息回忆以外的核心成果所造成的教学和评估问题。拟议的创新使重要的教育成果，如深刻的理解，认知（思维技能）和思维意识（元认知）可衡量。对这些结果的可靠评估对教育和社会具有广泛的意义，因为许多雇主都在努力解决高中和大学毕业生之间的技能差距以及21世纪世纪对成年人的专业，个人和社会需求。这项研究将认知科学，认识论，系统科学和复杂系统与人工智能的新发展相结合，包括机器学习和神经网络，并与NSF促进科学进步和促进国家繁荣的使命保持一致。该项目不仅有可能影响教育实践的核心原则-包括教师如何教学，学习者如何学习，以及我们如何测量和理解知识-而且还可能通过提高绘制和分析知识模式和常见结构的能力对科学产生影响，并普遍提高美国人的发展技能和能力。从价值80 - 150亿美元的教育市场开始，该项目有可能促进创造大量就业机会，并在一系列相关行业中产生重大的商业影响。SBIR第一阶段项目引入了一种视觉语法，用于在基于画布的环境中绘制想法，并提供了一个神经-基于网络的机制，用于沿着沿着许多维度定量比较复杂思想的表达，以促进思考、学习和评估的新方法。思想地图将被标记化和序列化，然后通过递归神经网络模型来生成编码的数字向量表示，这些数字向量表示在编码形式上具有意义上的可比性。一旦形成这种形式，将比较和评估向量的内容和结构相似性，并在许多方面提供见解：细节的深度，对关系的理解以及许多其他有意义的措施。这些载体以一种以前不可能的方式实现可扩展的，一致的，建设性的评估。在地图环境中，教师和学生将创建自己的地图，或实时合作。当用户创建地图时，我们使用生成的矢量来分析内容和结构，然后提示用户从新的角度思考他们的主题。作为一种评估手段，教师将创建地图活动，让学生完成，然后将学生地图与代表对主题完整理解的标准和目标地图进行定量比较。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This SBIR Phase I project will address the pedagogical and evaluative problems caused by our current inability to measure core outcomes beyond information recall. The proposed innovation makes important educational outcomes such as deep understanding, cognition (thinking skills) and awareness of thinking (metacognition) measurable. Reliable assessment of these outcomes has broad significance to education and society writ large, as many employers struggle with skills gaps between high school and college graduates and the professional, personal and societal demands on adults in the 21st Century. This research combines cognitive science, epistemology, systems science, and complex systems with new developments in artificial intelligence, including machine learning and neural networks, and aligns well with the NSF's mission to promote progress in science and advance national prosperity. This project not only has the potential to impact core tenets of educational practice - including how teachers teach, how learners learn, and how we measure and understanding knowledge - but also may have impact on science through increased ability to map and analyze patterns and common structures in knowledge and generally for Americans to increase their developmental skills and abilities. Beginning in an educational market valued at $8-15 billion, this project has the potential to catalyze significant job creation and have significant commercial impact across a range of related industries.This SBIR Phase I project introduces a visual grammar for mapping ideas in a canvas-based environment, and provides a neural-network based mechanism for quantitatively comparing expressions of complex ideas along many dimensions to facilitate a new approach to thinking, learning, and assessment. Maps of ideas will be tokenized and serialized and then fed through a recurrent neural network model to produce encoded numeric vector representations that are meaningfully comparable in their encoded form. Once in this form, vectors will be compared and evaluated for content and structure similarity, and insights offered in many dimensions: depth of detail, understanding of relationships, and numerous other meaningful measures. These vectors enable scalable, consistent, constructive assessment in a way not previously possible. In the mapping environment, teachers and students will create maps either on their own, or collaboratively together in real-time. As users create maps, we use the generated vectors to analyze both content and structure, and then prompt users to think about their subject matter from new perspectives. As a means of evaluation, teachers will create mapping activities for students to complete, and then student maps will be quantitatively compared to the standards and target maps which represent the complete understanding of the topic.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.