CHS: Medium: Collaborative Reearch: Bio-behavioral data analytics to enable personalized training of veterans for the future workforce

p该项目通过提供消除对退伍军人的隐性偏见和误解所需的经验知识，促进退伍军人在未来工作环境中的公平和道德待遇，并为退伍军人在未来的劳动力中获得和保持竞争力职位做好准备。尽管他们有很强的职业道德和奉献精神，美国的许多退伍军人仍然面临着参加文职劳动力的主要障碍。离职后，服务人员经常参加为期一周的过渡援助计划，充其量，可以被描述为一个方便的“一刀切”的解决方案。研究退伍军人在进入这个动态变化的就业市场的局限性是稀缺的，并没有提供一个充分了解退伍军人所面临的挑战，以及他们在求职面试时的思路。本研究收集经验证据，以了解退伍军人在文职工作面试中的共同感受，想法和潜在的弱点。该项目还提供了一种由人工智能支持的初步辅助技术，以量身定制和包容的方式促进退伍军人的面试技能，最终为他们未来的劳动力做好准备，并扩大他们在传统上代表性不足的领域的参与，例如计算。除了面试培训，通过与行业的有效合作，这项工作创造了教育材料，以促进退伍军人的未开发优势，如承诺，可靠性和责任感，潜在的雇主，从而改变就业招聘文化，并为退伍军人提供更多的机会在未来的工作环境。p p该项目通过计算科学和行为科学之间的合作来探索上述目标，以获得对退伍军人在平民采访中的经历的新见解，并设计新技术来支持退伍军人完成这项任务。研究工作将以三个技术目标进行。第一个目标是通过焦点小组讨论和退伍军人与行业代表的真实访谈收集数据，以确定访谈期间的挑战性遭遇。数据包括行为反应，生理反应，和主观评估的采访者和受访者，这是检查与采访设置，并进一步三角。第二个目标将探索受访者的时刻到时刻的压力的基础上，他们的发声，视觉表达和生理反应的量化措施。这些量化的措施，初步设计的培训干预措施，可以帮助退伍军人应对压力，在求职面试培训。第三个目标是考察受访者与面试官互动的能力。特别是，研究人员将开发自然语言处理和情感计算的新方法，用于检测军队特有的过于正式的会话语言，以及来自声学和视觉线索的交互的社会方面的退化。p p该奖项反映了NSF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。/ p

pThis project promotes fair and ethical treatment of veterans in the future job landscape by providing the empirical knowledge needed to remove implicit bias and misconceptions against veterans and prepare veterans for obtaining and maintaining competitive positions in the future workforce. Despite their strong work ethic and dedication, many veterans in the U.S still face major barriers to participating in the civilian workforce. After separation from duty, service members often participate in a week-long transition assistance program that, at best, can be described as a convenient "one-size-fits-all" solution. Research studying the limitations of the veteran population in entering this dynamically changing job market is scarce and does not provide a full understanding of the challenges faced by the veteran population as well as their train of thought during the time of the job interview. This project gathers empirical evidence to understand veterans' common feelings, thoughts, and potential weaknesses in social effectiveness skills during the civilian job interviews. The project further provides a preliminary assistive technology enabled by artificial intelligence for promoting veterans' interview skills in a tailored and inclusive manner, ultimately preparing them for the future workforce and broadening their participation in fields where they are traditionally underrepresented, such as computing. In addition to interview training, through effective partnerships with industry, this work creates educational materials for promoting unexplored strengths of the veteran population, such as commitment, reliability, and sense of duty, to the potential employers, thereby changing the job hiring culture and providing veterans with more opportunities in the future job landscape./p pThis project explores the above goals through a collaboration between computational and behavioral sciences for acquiring new insights into veterans' experiences during civilian interviews and designing novel technologies for supporting veterans in this task. The research work will be carried out with three technical aims. The first aim is on data collection through focus group discussions and real-life interviews of veterans with industry representatives to identify challenging encounters during the interview. Data include behavioral reactions, physiological reactivity, and subjective assessments of both the interviewer and interviewee, which are examined in association with the interview setting and are further triangulated. The second aim will explore quantifiable measures of interviewees' moment-to-moment stress based on their vocalizations, visual expressions, and physiological reactivity. These quantifiable measures are employed for the preliminary design of training interventions that can assist veterans on coping with stress during the job interview training. The third aim will examine the interviewee's ability to engage with the interviewer. In particular, the researchers will develop new methods in natural language processing and affective computing for detecting overly formal conversational language specific to the military, as well as degradation in social aspects of the interaction from acoustic and visual cues./p pThis 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./p