EXP: Collaborative Research: Extracting Salient Scenarios from Interaction Logs (ESSIL)

EXP：协作研究：从交互日志中提取显着场景 (ESSIL)

• 批准号: 1623091
• 负责人: Andee Rubin
• 金额: $ 32.5万
• 依托单位: TERC Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623091&HistoricalAwards=false
• 关键词: EXP; Collaborative; Research; Extracting; Salient

The Extracting Salient Scenarios from Interaction Logs (ESSIL) project proposes to develop a new type of educational technology to support students' learning about complex systems from their participation in a multi-person immersive simulation. Many important challenges we face today as a society -- including responding to climate change, managing global economies, city planning, disease outbreaks -- are "complex systems" problems, meaning that important phenomena in each (for instance trends in weather, stock bubbles, traffic jams, disease transmission) result not from a single cause, but because many small causes combine together. Participating in a simulation has the potential to help students understand the principles of complex systems, but because different principles surface depending on how each simulation unfolds, it can be difficult for teachers to adjust their lesson plans on the fly to highlight the principles that emerge in a given simulation run. To address this challenge, ESSIL will develop methods to create "automatic salient recaps," as a way to help learners and their teachers make better sense of simulations. These recaps, which will be automatically generated, provide a story of "what happened" in the simulation in a way that both helps students remember their experience and reveals important scientific principles. Teachers and other facilitators will use these recaps, along with an accompanying discussion guide, to support productive learning conversations about the scientific principles incorporated in a simulation. The recaps will be developed for a large-scale immersive simulation installed at the New York Hall of Science (NYSCI), potentially improving the educational experience of thousands of daily visitors. The capabilities developed to produce them have widespread applicability, because logs of student interactions are routinely produced by many educational systems. The project is supported by the Cyberlearning and Future Learning Technologies Program, which 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. The immersive simulation context for the project is Connected Worlds, an embodied, multi-person ecology simulation at NYSCI, with pedagogical goals around sustainability and systems thinking. Using logs from groups of students interacting with Connected Worlds, ESSIL will construct selective recaps of their experience that both are personally salient to them (by including memorable details of their experience) and have explanatory coherence (to enable their discussion of important interconnections in the simulation's underlying model). Artificial Intelligence-based methods will be developed to 1) identify salient changes in the state of the simulation during student interaction and 2) construct qualitative models of causal chains that could have led to these changes. These qualitative models will be used to generate salient recaps and discussion guides based on them, which will be provided to teachers whose classes are visiting NYSCI. The effectiveness of the innovation will be investigated by comparing visiting students' conversations with and without ESSIL-generated discussion supports and by interrogating their resulting models of the Connected Worlds system through concept maps.

从交互日志中提取突出场景(ESSIL)项目建议开发一种新型的教育技术，以支持学生通过参与多人沉浸式模拟来学习复杂系统。我们今天作为一个社会面临的许多重要挑战--包括应对气候变化、管理全球经济、城市规划、疾病爆发--都是“复杂的系统”问题，这意味着每个问题中的重要现象(例如天气趋势、股票泡沫、交通拥堵、疾病传播)不是由单一原因造成的，而是因为许多小原因结合在一起。参加模拟有可能帮助学生理解复杂系统的原理，但由于不同的原理根据每个模拟的展开方式而有所不同，因此教师可能很难动态地调整他们的教案，以突出给定模拟运行中出现的原理。为了应对这一挑战，ESSIL将开发出创建“自动显著复述”的方法，以帮助学习者和他们的老师更好地理解模拟。这些自动生成的摘要以一种既帮助学生记住他们的经历又揭示了重要的科学原理的方式，提供了一个关于模拟中发生的事情的故事。教师和其他辅导员将使用这些摘要以及附带的讨论指南，来支持有关模拟中包含的科学原理的富有成效的学习对话。这些摘要将为安装在纽约科学馆(NYSCI)的大规模沉浸式模拟而开发，可能会改善数千名日常参观者的教育体验。为制作它们而开发的能力具有广泛的适用性，因为许多教育系统通常都会生成学生互动的日志。该项目得到了网络学习和未来学习技术计划的支持，该计划为有助于展望下一代学习技术的努力提供资金，并促进我们对人们在技术丰富的环境中如何学习的了解。网络学习探索(EXP)项目通过设计和构建新的学习技术，并研究它们促进学习的可能性和有效使用它们的挑战，来探索新类型的学习技术的可行性。该项目的沉浸式模拟环境是互联世界，这是NYSCI的一个具体化的多人生态模拟，围绕可持续发展和系统思维的教学目标。使用来自与互联世界互动的学生小组的日志，ESSIL将有选择地重述他们的经历，这些经历对他们个人来说都是重要的(通过包括他们的经历的令人难忘的细节)，并具有解释性连贯性(使他们能够在模拟的基础模型中讨论重要的相互联系)。基于人工智能的方法将被开发来1)识别学生交互过程中模拟状态的显著变化，以及2)构建可能导致这些变化的因果链的定性模型。这些定性模型将被用来生成重要的摘要和基于它们的讨论指南，这些将被提供给访问NYSCI的班级的教师。这项创新的有效性将通过比较来访学生在有和没有ESSIL生成的讨论支持下的对话，以及通过概念图询问他们产生的互联世界系统模型来调查。