SBIR Phase II: A System for Enhancing Metacognitive and Problem-Solving Abilities in Engineers

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力涉及降低技术公司的成本以及增加来自各种背景的人获得尖端工程工作的机会。 该产品的商业化将为市场提供一个创新的自动化解决方案，减少招聘经理在招聘和培养最佳候选人上花费的时间。 到目前为止，元认知技能开发一直是亲自导师的职责范围-这种做法为来自历史上在技术方面代表性不足的背景的个人提供了较少的工程职业生涯，因为他们通常无法获得亲自的工程师导师。一个以自动化方式向用户提供元认知益处的可扩展系统可以通过减少对亲自指导的需求来创造更多的技术教育机会。该产品还将使求职者能够通过加强“难以教授”的实践来提高他们的工程潜力，这些实践将专业工程师与有抱负的工程师区分开来。总的来说，在第二阶段商业化的产品旨在增加更广泛的参与工程职业和更公平的一套招聘做法在技术公司。这个小企业创新研究（SBIR）第二阶段项目带来了尖端的认知科学承担人工候选人筛选和持续的员工提升技能的问题。它首次将智能辅导系统（ITS）与申请人跟踪系统（ATS）结合起来，为那些元认知特征与科技雇主需求密切匹配的个人打开了建立人才管道的大门。 该项目将认知科学领域的关键进展与软件设计的最佳实践相结合，以便在基于计算机的学习环境（CBLE）中创建新颖的用户交互。 目标包括：1）从用户交互中收集微数据（也称为“跟踪数据”），以自动推断用户的元认知特征，以及2）创建旨在促进元认知增长的输入。 将实时跟踪数据与元认知能力相关联是一个新的研究领域，直到最近才被越来越多的CBLE采用。 该项目有助于认知科学和人机交互领域的验证新方法测量元认知对现有的静态回顾技术。该项目还开创了通过CBLE互动提供个性化元认知增长的新模式。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project involves both reduced costs for technology companies as well as increased access to cutting-edge engineering jobs for people from all backgrounds. Commercializing this product will provide the market with an innovative, automated solution that reduces the time hiring managers spend on recruiting and growing the best candidates. Until now, metacognitive skill development has been the purview of in-person mentors – a practice that provides fewer on-ramps into engineering careers for individuals from backgrounds historically underrepresented in technology as they often lack access to in-person engineer mentors. A scalable system which delivers metacognitive benefits to users in an automated way may create greater access to technology education by reducing the need for in-person mentorship. The product will also enable job seekers to enhance their engineering potential by strengthening the "hard to teach" practices which distinguish professional engineers from aspiring ones. Overall, the product commercialized during Phase II seeks to increase broader participation in engineering careers and a more equitable set of hiring practices in technology companies.This Small Business Innovation Research (SBIR) Phase II project brings cutting-edge Cognitive Science to bear on the problems of manual candidate screening and ongoing employee upskilling. For the first-time, it marries an Intelligent Tutoring System (ITS) with an Applicant Tracking System (ATS) and opens the door to build talent pipelines for individuals whose metacognitive traits match closely with the needs of tech employers. The proposed project combines key advances in the field of Cognitive Science with best practices in software design in order to create novel user interactions inside a Computer-Based Learning Environment (CBLE). Objectives include: 1) collecting microdata (also called "trace data") from user interactions to make automated inferences about the user's metacognitive traits and 2) creating inputs designed to foster metacognitive growth. Correlating real-time trace data with metacognitive ability is a new area of research enabled only recently by the increased adoption of CBLEs. The proposed project contributes to the fields of Cognitive Science and Human-Computer Interaction by validating novel methods measuring metacognition against existing static look-back techniques. The project also pioneers new modes of delivering personalized metacognitive growth through CBLE interaction.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.