I-Corps Teams: STEM Skills Genome

I-Corps项目更广泛的影响/商业潜力是通过更好地将技能和先天兴趣与学位课程，特别是新颖的STEM课程结合起来，提高学生和最终员工的参与度。为了应对对新型工作岗位日益增长的需求，计算基因组学和音乐技术等学位课程将历史上截然不同的领域结合在一起。这个项目使学生能够发现与他们相关的传统和新兴专业。通过在早期提供可操作的见解和更好的利益一致性，这减少了大学期间换专业的摩擦（目前约为80%），并开始缓解工作场所普遍存在的缺乏参与度（约为70%）。与传统的将gpa、考试成绩、非结构化论文和简历与学校、专业和职位空缺相匹配的方法相比，这个项目可以彻底改变学生识别、培训和准备未来职业的方式。这个I-Corps项目是基于一种名为“技能基因组”的统一多维数据表示方法的新专利技术。技能基因组可以被几个机器学习模型利用，允许捕获和建模复杂的学生数据输入，如批判性思维、沟通、兴趣、领导力等。通过使几个机器学习模型可以访问图的结构，该方法提高了准确性，相关性并促进了规模。具体来说，这能追踪学生吗？因此，他们可以最好地确定所选职业所需技能的时间和顺序。此外，通过大规模匹配个人资料，可以对潜在的职业产生新的见解，以及实现这些职业所需的下一步措施。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 impact/commercial potential of this I-Corps project is to increase student and eventually, employee engagement, by better aligning skills and innate interests with degree programs, particularly novel STEM programs. In response to increasing demand for new types of jobs, degree programs such as Computational Genomics and Music Technology have combined fields that have historically been distinct. This project enables students to discover both traditional and emerging majors relevant to them. By providing actionable insights and better alignment of interests early on, this decreases the friction of changing majors (~80% currently) during college and begins to mitigate widespread lack of engagement (~70%) in the workplace. Compared with traditional methods that match GPAs, test scores, unstructured essays, and resumes, with schools, majors, and job openings, this project could revolutionize the way students identify, train and prepare for their future careers.This I-Corps project is based on a novel patent-pending technology of a unified multi-dimensional data representation method called Skills Genome. Skills Genome can be exploited by several machine learning models, allowing for capturing and modeling complex student data inputs such as critical thinking, communication, interests, leadership, etc. with temporal graphs. By making the structure of the graph accessible to several machine learning models, the method improves accuracy, relevance and facilitates scale. Specifically, this tracks the student?s development timeline, so they can best determine the timing and sequencing of skills needed for their chosen careers. Further, by matching profiles at scale, new insights into potential careers can be surfaced, along with the next steps needed to achieve them.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.