Collaborative Research: Human-Technology Partnership Supporting Career Path Exploration and Navigation

就业形势正在变化和扩大，随着个人在职业准备和继续教育方面做出决定，这带来了不确定性。该项目开发了一项技术，为大学生在一个庞大而复杂的决策空间中获取高质量的信息和指导提供指导。该方法包括整合人类职业教练或同龄人的社会支持，以增加解决问题和决策过程中的信心和动力。该方法基本上是数据驱动的，建立在计算建模的基础上，同时受到人类行为理论的启发，体现在一种名为社会敏感强化学习(SSRL)的新型机器学习范式中。基于这一新的范式，系统将生成指导，以支持学生，同时对学生的需求和偏好敏感，因此学生接受指导并从中受益的可能性很高。该项目以走上或推进STEM职业道路的非传统学生为目标，旨在扩大对STEM的参与。该项目有助于人机交互和机器学习，并在其人类交互建模和学习的社会技术支持方面采取了跨学科的方法。它包括一个三管齐下的解决方案：(1)计算模型链提出新的计算范例，不断绘制最佳实践图，并为数据驱动的建议提供信息，以支持有效的决策；(2)行为研究链通过定性和定量调查提供洞察力，揭示与指导是否被接受以及它如何影响职业发展成功相关的建议和推荐者的属性；以及(3)智能教练开发链体现了CareerScope智能教练代理(ICA)中其他两个链的发现，旨在作为一种社会技术解决方案，导致影响学生决策。与西部总督大学(WGU)的伙伴关系通过与西部总督大学的学生进行研究并在西部总督大学平台上进行部署，促进了研究向实践的大规模转化。虽然研究将在单一平台内进行，但技术创新最终将整合到广泛的平台中。该项目的成果将使人们更好地了解国际能力评估在指导学习者，特别是非传统学习者的职业道路方面的局限性和机会。这些结果可以告诉在线大学和实体大学如何最好地指导/培训/指导学生，或者将同行/专业指导和同行互动整合到当前的建议实践中。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 employment landscape is changing and expanding, creating uncertainty as individuals navigate decisions regarding career preparation and continuing education. This project develops technology to provide guidance for college students to access high quality information and guidance within a large and complex decision space. The approach includes integrating social support from human career coaches or peers to increase confidence and motivation in the problem solving and decision making process. The approach is fundamentally data driven, built upon computational modeling while informed by theories of human behavior, embodied within a novel machine learning paradigm termed Socially-Sensitive Reinforcement Learning (SSRL). Based on this new paradigm, the system will generate guidance to support students while being sensitive to student needs and preferences so that there is a high probability that students will accept and benefit from the guidance. The project targets nontraditional students who embark upon or advance STEM career paths and aims to broaden participation in STEM.The project contributes to both human-computer interaction and machine learning and takes an interdisciplinary approach in its modeling of human interaction and sociotechnical support for learning. It comprises a three pronged solution: (1) a computational modeling strand proposes new computational paradigms that continually map best practices and inform data-driven recommendations in support of effective decision making; (2) a behavioral research strand provides insight through qualitative and quantitative investigations, uncovering properties of recommendations as well as recommenders that are associated with whether guidance is accepted and how it influences success in career advancement; and (3) an intelligent coach development strand embodies findings from the other two strands in the CareerScope Intelligent Coach Agent (ICA), designed as a sociotechnical solution leading to impact student decision making. The partnership with Western Governor's University (WGU) facilitates transition of research into practice at large scale through research with WGU students and deployment on the WGU platform. While the research will be housed within a single platform, the technical innovations will ultimately be integrated into a wide range of platforms. The results of this project will broaden understanding of the limitations and opportunities for ICAs to guide learners, particularly non-traditional learners, through their career paths. These results can inform both online and brick-and-mortar universities how to best coach/train/mentor students or integrate peer/professional mentorship and peer interaction into current advising practices.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.