SBIR Phase I: A novel method to scaling mentoring and career development in Institutes of Higher Education

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是通过学术和职业指导来提高中学后学生的成功。大量关于职业导航的研究已经研究了中学后教育，职业准备（毕业时了解可行的职业道路）及其相互联系对于越来越多的第一代，低收入和代表性不足的学生的重要性。随着越来越多的本科学位课程提供，以应对不断变化的未来工作和学生与辅导员的比例为1：1,800在公立大学，职业指导和学术导航风险不可用。因此，在疫情期间，为有色人种学生和低收入背景学生提供服务的高等教育机构（IHE）的入学率下降幅度远远超过了以白色学生为主的同龄机构。拟议的平台旨在提高IHE学生潜在职业和学术道路能力的可见性，可访问性和可扩展性。该平台设想通过在自我效能方面相似的近同龄人榜样来做到这一点。到2030年，超过8500万个工作岗位可能会空缺，拟议中的平台可能会通过扩大人才缺口来帮助缓解部分短缺。该项目的智力价值在于该公司的专利技术，即统一的多维数据表示模型，为每个用户创建“能力指纹”。数据表示方法使更好的机器学习模型能够从学生传统和非传统学习经验的非结构化数据中“推断”能力，而不是学位，专业，平均成绩（GPA）或考试成绩。该平台使用一致的，可扩展的，能力命名通过传统的学术和课堂外的经验获得的硬技能和软技能，以发现学生的学习能力可能需要的学术职业道路。在采用这种技术用于未来的跨学科/跨学科工作方面存在重大的技术挑战：这样的平台需要一个强大的，更大的数据集来评估匹配的相关性，在学科不可知的背景下。减少这种可变性是拟议的研究和开发所要克服的关键技术风险。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知识价值和更广泛的影响审查标准进行评估来支持。

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance. A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students. With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1: 1,800 in public colleges, career guidance, and academic navigation risk being unavailable. As a result, during the pandemic, Institutes of Higher Education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly White peer institutions. The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs. The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy. With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture.The intellectual merit of this project is in the company’s patented technology of a unified, multi-dimensional, data representation model that creates a ‘competency fingerprint’ for each user. The data representation method enables better machine learning models to ‘infer’ competency from unstructured data of a student’s traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores. The platform uses a consistent, scalable, competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students’ learning competencies may be in demand. There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context. Reduction of this variability is the key technical risk to be overcome by the proposed research and development.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.