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大学生的成功。大量关于职业导航的研究已经研究了高等教育、职业准备（在毕业时了解可行的职业道路）及其相互联系对越来越多的第一代、低收入和代表性不足的学生的重要性。随着本科学位课程的增加，以应对未来不断变化的工作，公立大学的学生与辅导员的比例为1:18 00，职业指导和学术导航面临着不可用的风险。因此，在疫情期间，为有色人种和低收入背景学生提供服务的高等教育学院（IHEs）的入学人数下降幅度远远超过了以白人学生为主的同类机构。该平台旨在提高高等教育学院学生潜在职业和学术道路能力的可见性、可访问性和可发现性。该平台设想通过在自我效能感维度上相似的近同伴角色榜样来实现这一目标。到2030年，全球将有超过8500万个工作岗位空缺，该平台可能会通过扩大人才缺口，帮助缓解这一短缺问题。该项目的智力优势在于该公司的专利技术，该技术是一种统一的、多维的数据表示模型，可以为每个用户创建“能力指纹”。数据表示方法使机器学习模型能够更好地从学生传统和非传统学习经历的非结构化数据中“推断”能力，而不是从学位、专业、平均绩点（gpa）或考试成绩中推断。该平台使用一致的、可扩展的能力命名法，对通过传统学术和课堂外经验获得的硬技能和软技能进行命名，以发现学生学习能力可能需要的学术职业道路。在未来的跨学科工作中采用这种技术存在一个重大的技术挑战：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，在一个与学科无关的环境中。减少这种可变性是拟议的研究和开发需要克服的关键技术风险。该奖项反映了美国国家科学基金会的法定使命，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/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.