Enhancing flexible STEM thinking by generating interactive diagrams at scale

通过大规模生成交互式图表来增强灵活的 STEM 思维

• 批准号: 2119007
• 负责人: Joshua Sunshine
• 金额: $ 84.94万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119007&HistoricalAwards=false
• 关键词: Enhancing; flexible; STEM; thinking; generating; Enhancing; flexible; STEM; thinking; generating

Math and science diagrams improve learning, both when diagrams are delivered with instruction, and when they are created for self-explanation. The resulting learning is often more flexible. For example, students that practice diagramming are better at transferring their learning from the problems they have explicitly practiced to more open-ended problems. Unfortunately, diagrams are too uncommon in instructional materials, especially practice problems. This is primarily because diagrams are much harder to produce than text and symbols. Teaching at scale adds further challenges. Ideally, e-learning platforms should deliver different problems to different students. These problems should be tailored to knowledge components, prevent cheating by copying, and be tuned the amount of practice to student needs. Problem templating systems aren’t built for diagrams and grading student-authored diagrams is hard to do even manually. To address the challenge, this project aims to develop a new tool for generating diagrammatic instructional content. This tool developed by the project will enable content authors to generate large problem sets by example. User will author one or two problems and the tool will synthesize a problem set from the examples. The project introduces efficient interaction techniques for viewing and editing these sets. The project will collect data from teachers and students to guide, refine, and evaluate the design of the tool. The project will conduct six studies, three will focus on the effectiveness of the tool in supporting authoring of educational content and three that focus on the impact of the resulting problems on student learning. These studies will enhance our understanding of when and how diagrams can be used to increase student understanding of science, technology, engineering and math (STEM) principles and lead to more flexible STEM thinking.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数学和科学图可以改善学习，既可以通过指示交付图表，又是为了自我解释而创建的。由此产生的学习通常更灵活。例如，练习图解的学生可以更好地从他们明确实践的问题中转移学习，从而将他们的知识转移到更大的问题上。不幸的是，图表在教学材料中并不常见，尤其是练习问题。这是主要的，因为图比文本和符号要难得多。大规模的教学增加了进一步的挑战。理想情况下，电子学习平台应为不同的学生提供不同的问题。这些问题应针对知识组件量身定制，通过复制来防止作弊，并对学生需求进行调整。问题模板系统不是为图表而设计的，并且很难手动进行评分。为了应对挑战，该项目旨在开发一种用于生成诊断教学内容的新工具。该项目开发的工具将使内容作者能够以身作则生成大型问题集。用户将发表一个或两个问题，该工具将从示例中综合一个问题。该项目介绍了用于查看和编辑这些集合的有效交互技术。该项目将从教师和学生那里收集数据，以指导，完善和评估该工具的设计。该项目将进行六项研究，三项将重点介绍该工具在支持教育内容创作的有效性上，而三个专注于由此产生的问题对学生学习的影响。这些研究将增强我们对何时以及如何使用图表来提高学生对科学，技术，工程和数学（STEM）原理的理解，并导致更灵活的STEM思维。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的审查标准来评估，通过评估来诚实地支持。