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

CHS：中：协作研究：生物行为数据分析，为未来的劳动力提供退伍军人的个性化培训

• 批准号: 1955721
• 负责人: Ani Nenkova
• 金额: $ 10.68万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955721&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Reearch; Bio

This 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.This 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.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.

该项目通过提供消除对退伍军人的隐性偏见和失误所需的经验知识来促进退伍军人对退伍军人的公平和道德待遇，并准备退伍军人在将来的劳动力中获得和维持竞争地位。尽管他们有着强大的职业道德和奉献精神，但美国许多退伍军人仍然面临参与平民劳动力的主要障碍。与职责分开后，服务成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种方便的“单一适合所有”解决方案。研究退伍军人在进入这个动态不断变化的就业市场时的局限性的研究很少，并且没有充分了解退伍军人人口所面临的挑战以及他们在工作面试时期的思想列车。该项目收集了经验证据，以了解退伍军人在平民工作访谈中社会效能技能中的共同感受，思想和潜在的弱点。该项目进一步提供了由人工智能以量身定制和包容的方式促进退伍军人的面试技巧的初步辅助技术，最终为未来的劳动力做准备，并扩大他们在传统上代表性不足的领域（例如计算）的参与。除了通过与行业的有效合作培训，还为潜在的雇主提供了教育材料，以促进退伍军人人口的意外优势，例如承诺，可靠性和责任感，从而改变雇用工作的文化，从而改变退伍军人，并为退伍军人提供更多的机会，从而在未来的工作中探索以上的努力。平民访谈和设计新型技术，以支持退伍军人。研究工作将以三个技术目标进行。第一个目的是通过焦点小组讨论和对退伍军人的现实生活访谈，在采访中代表挑战赛。数据包括行为反应，身体反应性以及对访调员和受访者的主题评估，这些评估与访谈环境相关，并进一步进行了三角测量。他们将根据他们的发声，视觉表达和身体反应性来探讨受访者对瞬间压力的可量化量度。这些可量化的措施是针对培训干预措施的初步设计，可以帮助退伍军人在面试培训期间应对压力。第三个目标将检查受访者与面试官互动的能力。 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.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.