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

小型企业创新研究(SBIR)第二阶段项目的更广泛/商业影响是增加女性在科学、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。造成女孩和妇女在STEM中代表性不足的因素往往在她们接受教育的早期就产生影响，并超出了传统的课堂环境。很少有解决办法具体解决父母需要的支持，以促进STEM非正式学习，以吸引他们年幼的女儿。该项目旨在提供个性化的学习途径，旨在促进女孩在学习之旅的早期获得积极的STEM经历，以便她们更有可能从事STEM职业。该项目旨在提供一个使用新的推荐系统的学习平台，该系统应用了关于年轻女孩独特的STEM学习需求的惊讶和好奇心的算法建模以及最佳做法。本项目将解决的主要技术障碍如下：(1)改进算法模型，将其用于生成引起好奇心的建议序列，其方式既能增加人们对STEM的兴趣，又能提高对STEM的学习和职业意识；(2)通过增强数据模型的功能和改进数据模型来扩展数据集和数据表示；(3)将学习者的兴趣输入可视化和游戏化；(4)实现吸引人的用户界面和体验。算法模型的改进预计将扩大关于行为改变建议、面向年轻目标受众的推荐系统以及人工智能系统中的惊喜和好奇心建模的研究知识。该解决方案最终将提供个性化的STEM职业探索的商业应用，特别适合年轻女性。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/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is an increase in 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. The factors contributing to the underrepresentation of girls and women in STEM often take effect early in their education and extend beyond traditional classrooms settings. Very few solutions specifically address the support needed by parents to facilitate STEM informal learning in a way that is engaging to their young daughters. This project intends to deliver personalized learning pathways designed to catalyze positive STEM experiences for girls early in their learning journeys so that they are more likely to embrace STEM careers. This project seeks to deliver a learning platform that operates using a novel recommender system, which applies algorithmic modeling of surprise and curiosity as well as best practices regarding the unique STEM learning needs of young girls. The main technical hurdles that will be addressed in this project are as follows: (1) refinement of algorithmic model, which will be applied to generate recommendation sequences that elicit curiosity in manner that both increases interest in STEM and prompts additional STEM learning and career awareness; (2) expansion of a dataset and data representation through the enhanced features and improvements to the data model; (3) visualization and gamification of learner interest inputs and (4) implementation of an engaging user interface and experience. The refinement of algorithmic models is expected to expand the research knowledge on recommendations for behavior change, recommender systems for a young target audience, and surprise and curiosity modeling in artificial intelligence systems. The solution will ultimately deliver a commercial application that personalizes STEM career exploration, particularly suited for young women.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.