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

智能、互动和高度网络化的机器——人们越来越多地与之分享他们的自主权和代理权——正在成为景观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职业，需要洞察力和行动来获得提高生产力和增加就业机会的好处，并减轻社会成本。在劳动力需求快速变化的环境中，这些创新对终身学习、技能评估和职业培训/再培训也具有重大意义和潜在价值。由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、技术和工作关系的关键挑战和研究要求，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，以形成新的和扩展的框架，以应对科学和社会的挑战和机遇。本次融合研讨会将探讨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学习和解决问题过程中人类情感，动机，元认知和认知的多模态，多通道传感器的基本理解，并有效地将这些数据整合到先进的学习技术环境（例如，智能辅导系统）中可操作的教育干预措施中。本研究的影响延伸到不同的学习环境、工作培训和再培训机会。将采用多阶段讲习班方法来探讨对多个工作部门的影响，其成果将跨越地理和学科界限广泛传播。

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.