SBIR Phase II: Science, Technology, Engineering, and Math (STEM) Career Literacy & Advocacy

该项目将提高对需要科学、技术、工程和数学(STEM)能力的职业的认识和看法。倡导是职业准备的关键组成部分，但目前的倡导者(父母、监护人、教育工作者或社区成员)往往无法告知学生潜在的职业选择。职业和劳动力准备计划资源有限，不能满足不同学习风格的需求，存在固有的偏见，主要侧重于合规，而不是学生能力建设。许多本来可以在STEM取得成功的代表不足的年轻人往往因为缺乏榜样而望而却步。如果年轻人理解了这些机会，他们就可以通过学术途径积累技能，为进入劳动力市场做好更好的准备。此外，教育工作者需要专业的学习经验和对学生的深入了解，以改善STEM的教和学。帮助个人选择有价值和合适的学位、培训和职业，将增加更高保留率的可能性。随着更多的人被激励去追求和留在STEM，纳税人将从更多的创新中受益，这反过来将提供税收来投资于医疗保健、国家安全、教育或人道主义援助等领域。该项目将解决以下技术挑战：收集和分发大量第三方STEM(科学、技术、工程和数学)资源数据(包括结构化和非结构化的)、为教育工作者和家庭开发信息管理系统、开发自适应学习技能模块、基于大数据、机器学习和预测建模技术的第二代智能推荐引擎，以及执行数据库挖掘和创建数据可视化以获得有意义的劳动力发展见解。此外，该项目将涉及受控实验和可用性测试，从而收集大量匿名和聚合的学生数据以及企业/教育实体的反馈。研发和实验的最终目标是验证由此产生的应用程序、预测模型、信息管理实践、宣传网络和数据可视化具有预期的结果，即提高学生在STEM职业意图和行动方面的直接和近期结果。

This project will improve the awareness and perception of careers that require science, technology, engineering, and math (STEM) competencies. Advocacy is a critical component of career readiness, yet current advocates (parents, guardians, educators, or members of the community) are often not in the position to inform students of potential career options. Career and workforce readiness programs are resource constrained, don't meet the needs of differing learning styles, have inherent bias, and are largely focused on compliance over student competency building. Many underrepresented youths who would otherwise succeed in STEM are often deterred by a lack of role models. If youth understood the opportunities, they could pursue academic pathways to amass skills that better prepare them to enter the workforce. Furthermore, educators need professional learning experiences and access to insights about their students in order to improve STEM teaching and learning. Helping individuals select rewarding and suitable degrees, training, and careers will increase the likelihood of higher job retention. As more individuals are inspired to pursue and stay in STEM, taxpayers will benefit from increased innovation which in turn will provide tax dollars to invest in such things as healthcare, national security, education, or humanitarian assistance.The project will address technical challenges of amassing and distributing massive amounts of 3rd party STEM (Science, Technology, Engineering, and Math) resource data (both structured and unstructured), developing an information management system for educators and families, developing adaptive learning skill modules, advancing a second generation smart recommendation engine based on big data, machine learning and predictive modeling techniques, and performing database mining and creating data visualizations to derive meaningful workforce development insights. In addition, the project will involve controlled experiments and usability tests whereby a large amount of anonymized and aggregated student data and business/education entity feedback will be collected. The ultimate goals of the R&D and experiments are to validate that the resulting application, predictive models, information management practices, advocacy networks, and data visualizations have the desired outcome of boosting immediate and near-term student outcomes regarding STEM career intentions and actions.