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的决策，造成了不确定性。 该项目开发技术，为大学生提供指导，在一个庞大而复杂的决策空间内获得高质量的信息和指导。该方法包括整合来自人类职业教练或同行的社会支持，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影响，体现在一种称为社会敏感强化学习（SSRL）的新型机器学习范式中。基于这种新的模式，该系统将产生指导，以支持学生，同时对学生的需求和偏好敏感，使学生很有可能接受并受益于指导。该项目的目标是那些走上或推进STEM职业道路的非传统学生，旨在扩大STEM的参与度。该项目有助于人机交互和机器学习，并采用跨学科的方法建模人类互动和社会技术支持学习。它包括一个三管齐下的解决方案：（1）计算建模链提出新的计算范例，不断映射最佳实践，并为支持有效决策的数据驱动建议提供信息;（2）行为研究链通过定性和定量调查提供洞察力，发现与指导是否被接受以及它如何影响职业发展成功相关的推荐和建议的属性;以及（3）智能教练开发链体现了CareerScope智能教练代理（伊卡）中其他两个链的发现，设计为影响学生决策的社会技术解决方案。与西部州长大学（WGU）的伙伴关系，通过与WGU学生的研究和在WGU平台上的部署，促进了研究向大规模实践的过渡。虽然这项研究将被安置在一个单一的平台上，但技术创新最终将被整合到一个广泛的平台上。这一项目的成果将扩大对初级商品协定的局限性和机会的了解，以指导学习者，特别是非传统学习者，通过他们的职业道路。这些结果可以告知在线和实体大学如何最好地指导/培训/指导学生或将同行/专业指导和同行互动融入当前的咨询实践。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智力价值和更广泛的影响力审查标准进行评估来支持。

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.