Convergence HTF: Collaborative: Workshop on Convergence Research about Multimodal Human Learning Data during Human Machine Interactions

融合 HTF：协作：人机交互过程中多模态人类学习数据的融合研究研讨会

• 批准号: 1854175
• 负责人: Roger Azevedo
• 金额: $ 1.25万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854175&HistoricalAwards=false
• 关键词: Convergence; HTF; Collaborative; Workshop; Research

Intelligent, interactive, and highly networked machines -- with which people increasingly share their autonomy and agency -- are a growing part of the landscape, particularly in regard to work. As automation today moves from the factory floor to knowledge and service occupations, insight and action are needed to reap the benefits in increased productivity and increased job opportunities, and to mitigate social costs. Such innovations also have significant implications and potential value for lifelong learning, skills assessments, and job training/retraining in an environment in which workforce demands are changing rapidly. The workshop supported by this award will promote the convergence of cognitive psychology, learning sciences, data science, computer science, and engineering disciplines to define key challenges and research imperatives of the nexus of humans, technology, and work with focus on human affect, motivation, metacognition, and cognition during learning and problem solving. Convergence is the deep integration of knowledge, theories, methods, and data from multiple fields to form new and expanded frameworks for addressing scientific and societal challenges and opportunities. This convergence workshop addresses the future of work at the human-technology frontier.The specific focus of this multi-phased workshop approach is to advance fundamental understanding of how to collect and analyze multimodal, multichannel sensor on human affect, motivation, metacognition, and cognition during learning and problem solving, and effectively integrate this data into actionable educational interventions in advanced learning technology environments (e.g., intelligent tutoring systems). The impacts of this research extend to a diverse range of learning environments, and job training and retraining opportunities. A multi-phased workshop approach will be used to explore the implications in multiple job sectors, and the outcomes will be broadly disseminated across geographic and disciplinary boundaries.

智能、交互和高度联网的机器--人们越来越多地与之分享他们的自主权和代理--正在成为环境中日益增长的一部分，特别是在工作方面。今天，随着自动化从工厂车间转移到知识和服务行业，需要洞察和行动，以获得提高生产率和增加就业机会的好处，并降低社会成本。在劳动力需求迅速变化的环境中，这些创新对终身学习、技能评估和工作培训/再培训也具有重大影响和潜在价值。该奖项支持的研讨会将促进认知心理学、学习科学、数据科学、计算机科学和工程学科的融合，以确定人类、技术和工作的关键挑战和研究需求，重点关注人类在学习和解决问题过程中的情感、动机、元认知和认知。融合是对来自多个领域的知识、理论、方法和数据的深度整合，以形成应对科学和社会挑战和机遇的新的和扩展的框架。这一融合研讨会讨论了人类-技术前沿的未来工作。这种多阶段研讨会方法的具体重点是促进对如何收集和分析关于人类在学习和解决问题过程中的情感、动机、元认知和认知的多模式、多通道传感器的基本理解，并将这些数据有效地整合到高级学习技术环境中的可操作教育干预中(例如，智能教学系统)。这项研究的影响延伸到各种不同的学习环境，以及工作培训和再培训机会。将采用分阶段研讨会的方法，探讨对多个工作部门的影响，其成果将跨地域和学科范围广泛传播。