SBIR Phase I: A Digital Platform That Engages Elementary Aged Girls In STEM Through Personalized Informal Learning

小型企业创新研究(SBIR)第一阶段项目的更广泛影响最终是增加女性在科学、技术、工程和数学(STEM)就业领域的代表性，使美国能够满足对STEM工人日益增长的需求，并保持在全球创新社区的竞争力。促进女孩和妇女在STEM中代表性不足的因素往往在教育早期就生效，并延伸到传统的课堂环境之外；这一解决办法具体涉及家长所需的支持，以促进STEM以一种对其年幼的女儿特别有吸引力的方式进行非正式学习。这个拟议的项目基于对非正式环境中学习的循证研究，将提供每月个性化的非正式学习计划，使父母能够在女孩的学习之旅早期为她们催化积极的STEM经历，使她们更有可能拥抱STEM职业生涯，并更好地在未来获得这些职业。这个小企业创新研究(SBIR)第一阶段项目将通过提供一个针对女孩独特兴趣的STEM非正式学习经历的渐进播放列表，改变女孩感知和参与STEM职业探索的方式。这是通过一个新颖的人工智能(AI)驱动的推荐系统来实现的，该系统从用户的偏好和属性中学习来推荐内容和学习体验，这旨在增加STEM女孩的信心和兴趣以及激发她们的好奇心。家长可以选择对推荐的计划提供即时反馈，进一步调整计划以满足孩子的需求，或者将选择与次要建议互换。拟议项目的目标是开发和测试排序推荐的算法模型，设计和构建原型应用程序，并进行研究以确定个性化推荐序列加深和/或扩展STEM知识和兴趣。为了指导开发，将通过比较使用人工推荐进行研究的结果和使用人工智能推荐器的结果来评估推荐器的效率。一旦算法模型建立，将进行事前和事后评估，以评估干预对STEM参与度和好奇心的影响，从而促进该领域知识的增长以及商业适用和及时的市场产品。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 of this Small Business Innovation Research (SBIR) Phase I project is ultimately to increase the representation of women in Science, Technology, Engineering, and Math (STEM) employment areas, enabling the US to meet the increasing demand for STEM workers and maintain competitiveness in the global innovation community. Factors promoting underrepresentation of girls and women in STEM often take effect early in education and extend beyond traditional classrooms settings; this solution specifically addresses the support needed by parents in order to facilitate STEM informal learning in a way that is particularly engaging for their young daughters. This proposed project, based on evidence-based research in learning in informal environments, will deliver monthly personalized informal learning plans to enable parents to catalyze positive STEM experiences for girls early in their learning journeys so that they are more likely to embrace STEM careers and better positioned to secure them in the future.This Small Business Innovation Research (SBIR) Phase I project will transform the way young girls perceive and engage in STEM career exploration by providing a progressive playlist of STEM informal learning experiences personalized to their unique interests. This is accomplished through a novel artificial intelligence (AI) driven recommender system that learns from user preferences and attributes to recommend content and learning experiences, which are meant to increase STEM confidence and interest for young girls as well as inspire their curiosity. Parents can opt to provide immediate feedback on the recommended plan that further tunes the plan to their child’s needs or swap selections with secondary recommendations. The proposed project’s objectives are to develop and test algorithmic model for sequenced recommendations, design and build the prototype application, and conduct research to determine that personalized recommendation sequences deepen and/or expand STEM knowledge and interests. To guide the development, efficacy of the recommender will be assessed by comparing results from conducting studies using human recommendations with results using the AI recommender. Once the algorithmic model is established, pre and post assessments will be conducted to assess impact of the intervention on engagement and curiosity with STEM, thereby contributing to an increase in knowledge in the field as well as a commercially applicable and timely marketplace product.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.