EXP: Fostering Self-Correcting Reasoning with Reflection Systems

EXP：通过反射系统促进自我纠正推理

• 批准号: 1623419
• 负责人: Michael Hoffmann
• 金额: $ 55万
• 依托单位: Georgia Tech Research Corporation
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623419&HistoricalAwards=false
• 关键词: EXP; Fostering; Self; Correcting; Reasoning

This research project is exploring how to support reasoning about wicked problems. These are societal important problems that are characterized by incomplete or contradictory knowledge, have a large body of differing opinion on the problem, have a large economic burden, and are intimately interconnected with other problems. An example of such a problem is poverty. Poverty is linked with education, nutrition to poverty, the economy with nutrition, etc. Reasoning about such problems and coming up with partial solutions is an important learning activity. One aspect of approaching wicked problems is through the use of reflection to guide argumentation. This project explores supporting reflection in undergraduate students with software that supports the reflection process and software that aims to improve the quality of arguments. This software builds upon both visualizations of arguments and a structured format, known as the Vee diagram, that structures good argumentation through a process of studying, questions synthesis, and finally analysis and reflection. More specifically, the researchers analyze how experts approach wicked problems, how they engage in reflection, and how they assess and improve the quality of their arguments. Results of these experiments with experts will be incorporated into Computer Supported Argument Visualization (CSAV) tools. The approaches explored in this project are of two types: the use of templates to trigger reflection and the use of scripts to provide a structure to reason about an issue. As a starting point, the researchers build upon the argumentation Vee diagram for the first approach and the AGORA software, which has been developed by the PI, as an approach for a script-based approach. Results of the experiments will contribute to an understanding of how reflective learning and self-correcting reasoning can be fostered by assessing specific features of reflection tools and interactions scripts. Research results enable known obstacles to self-improvement, such as students' implicit assumptions about the nature and certainty of knowledge and bias, to be addressed through educational interventions.

这个研究项目正在探索如何支持对邪恶问题的推理。这些重要的社会问题的特点是知识不完整或相互矛盾，对问题有大量不同的意见，有很大的经济负担，并与其他问题密切相关。这种问题的一个例子是贫穷。贫穷与教育有关，营养与贫穷有关，经济与营养有关，等等。对这些问题进行推理并提出部分解决方案是一项重要的学习活动。解决棘手问题的一个方面是通过使用反思来指导论证。这个项目探讨了如何用软件来支持大学生的反思过程，以及如何用软件来提高论证的质量。这个软件建立在论据的可视化和一种被称为“v”图的结构化格式的基础上，这种格式通过研究、问题综合、最后分析和反思的过程来构建良好的论据。更具体地说，研究人员分析了专家如何处理棘手的问题，他们如何进行反思，以及他们如何评估和提高他们的论点的质量。这些与专家的实验结果将被纳入计算机支持的论证可视化（CSAV）工具。本项目探索的方法有两种类型：使用模板触发反思，以及使用脚本提供对问题进行推理的结构。作为起点，研究人员建立了第一种方法的论证v图和由PI开发的AGORA软件，作为基于脚本的方法的方法。实验结果将有助于理解如何通过评估反思工具和交互脚本的特定特征来促进反思学习和自我纠正推理。研究结果可以通过教育干预来解决自我完善的已知障碍，例如学生对知识和偏见的性质和确定性的隐性假设。