EXP: Partners in Learning: Building Rapport with a Virtual Peer Tutor

EXP：学习伙伴：与虚拟同伴导师建立融洽关系

• 批准号: 1523162
• 负责人: Justine Cassell
• 金额: $ 55万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1523162&HistoricalAwards=false
• 关键词: EXP; Partners; Learning; Building; Rapport

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project seeks to understand, and capitalize on, how teachers or tutors build rapport with learners by building technologies that support rapport. Research will study what rapport with learners looks like, when students deploy rapport techniques, when and how deploying rapport techniques (whether by people or automated agents) increases learning, and how rapport evolves over time. The project will build software that can help measure rapport between learners and with computers. The project will begin by building a multimodal sensing rapport-detection system , based on recent advances in computer vision, signal processing, and machine learning which will automatically recognize audio and visual behaviors during learner interaction with an intelligent tutoring system. Human-human tutoring interactions will be used to guide development of the rapport detection system. Both short term and longitudinal analyses will be conducted using students working with an AI-based math tutor, focused on their visual behaviors (head gaze estimation will be used to measure facial action units and gestures like head nods or shakes, and mutual gaze between humans), verbal behaviors (using CoreNLP and other software to detect verbal utterances that represent rapport-related social constructs such as politeness, friendship, etc.), and entrainment behaviors (synchrony or asynchrony, divergence and convergence). The project will then design RAPT, the Rapport-Aligned Peer Tutor, which encompasses both the rapport detection system and an intelligent pedagogical agent that accounts for the persistent social states of rapport and non-rapport. Mockups/simulations of the interface will be used to test the designs before the full pedagogical agent is built. Trials will be conducted in 9-11 grade classrooms working with an intelligent geometry tutor using a two-iteration design-based research study.

网络学习和未来学习技术计划资助的努力将有助于设想下一代学习技术，并增进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索 (EXP) 项目通过设计和构建新型学习技术并研究其促进学习的可能性以及有效使用它们的挑战来探索新型学习技术的可行性。该项目旨在了解并利用教师或导师如何通过构建支持融洽关系的技术来与学习者建立融洽的关系。研究将研究与学习者的融洽关系是什么样的，学生何时运用融洽技巧，何时以及如何运用融洽技巧（无论是由人还是自动化代理）促进学习，以及融洽关系如何随着时间的推移而发展。该项目将构建可以帮助衡量学习者之间以及与计算机之间的融洽关系的软件。该项目将首先基于计算机视觉、信号处理和机器学习的最新进展，构建一个多模态传感融洽检测系统，该系统将在学习者与智能辅导系统交互期间自动识别音频和视觉行为。人与人之间的辅导互动将用于指导融洽检测系统的开发。短期和纵向分析都将由学生与基于人工智能的数学导师合作进行，重点关注他们的视觉行为（头部注视估计将用于测量面部动作单位和手势，如点头或摇头，以及人类之间的相互注视）、言语行为（使用 CoreNLP 和其他软件来检测代表与融洽相关的社会结构（如礼貌、友谊等）的言语话语），以及 夹带行为（同步或异步、发散和收敛）。然后，该项目将设计 RAPT，即融洽一致的同伴导师，它包含融洽检测系统和智能教学代理，该代理可以解释融洽和非融洽的持续社会状态。在构建完整的教学代理之前，将使用界面的模型/模拟来测试设计。试验将在 9 至 11 年级的教室中与智能几何导师合作，使用基于两次迭代设计的研究进行。