权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Enhancing Visualization Skills and Conceptual Understanding Using a Drawing-Recognition Tutoring System for Engineering Students

使用工程专业学生的绘图识别辅导系统增强可视化技能和概念理解

基本信息

批准号：
1725659
负责人：
Benjamin Caldwell
金额：
$ 13.74万
依托单位：
LeTourneau University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725659&HistoricalAwards=false
关键词：
Enhancing Visualization Skills Conceptual Understanding

项目摘要

Visual and spatial skills are important for scientific and engineering innovation. The ability to represent real systems through accurate yet simplified diagrams is a crucial skill for engineers. A growing concern among engineering educators is that students are losing both the skill of sketching and the ability to produce the free-body diagrams (FBDs) of real systems. These diagrams form the basis for various types of engineering analyses. To address this concern, investigators will redesign and test a cutting-edge educational technology for engineering concepts of statics and mechanics. The sketch-based technology developed at Texas A&M University, called Mechanix, enabled students to hand-draw FBDs, trusses, and other objects using digital ink and provided helpful feedback. The upgraded Mechanix software will include enhanced artificial intelligence (AI) to understand the sketches and provide immediate feedback to the student for individualized tutoring. Instructors will also receive real-time detailed information from the system so they can clarify misconceptions and guide students through problem solutions during classes. This free-hand sketch-based system will focus learning on the fundamental engineering concepts and not on how to use a software tool. These engineering concepts directly relate to a wide variety of designs including bridges, buildings, and trusses that are vital to the infrastructure of the nation's cities. The project will help prepare engineers with improved abilities to develop these designs that are essential in society.This project will aim to demonstrate the impact of the sketch-recognition based tutoring system on students' motivation and learning outcomes, both generally and among students of diverse backgrounds. The Mechanix system will be converted to an HTML5 format to work on all devices and expand its accessibility for institutions with various technological requirements. Additional AI algorithms will be developed to accommodate more types of statics problems, increased sketch-recognition accuracy and speed, and improved feedback mechanisms for instructors that merge performance information for the students in a class. The upgraded system will be studied in various engineering courses across five different universities, and introduced to over 2,500 students in engineering and related fields. The investigators will utilize controlled classroom experiments, digital data collection, pre/post concept testing, focus groups, and interviews to explore the external validity of Mechanix as a learning tool. Analysis of Covariance will be used to compare outcomes for students using Mechanix and students in control groups. Project outcomes and the Mechanix software will be shared through the project website, professional development workshops, and publications.

视觉和空间技能对于科学和工程创新非常重要。通过准确而简化的图表表示真实系统的能力对工程师来说是一项关键技能。工程学教育工作者越来越担心的是，学生们正在失去绘制草图的技能和制作真实系统的自由体示意图(FBD)的能力。这些图表构成了各种类型工程分析的基础。为了解决这一担忧，调查人员将重新设计和测试一种用于静力学和力学工程概念的尖端教育技术。这项由德克萨斯农工大学开发的基于草图的技术名为MECHNIX，使学生能够使用数字墨水手绘FBD、桁架和其他物体，并提供有用的反馈。升级后的MECHNIX软件将包括增强的人工智能(AI)，以理解草图并为学生提供即时反馈，以便进行个性化辅导。教师还将从系统接收实时详细信息，以便他们可以澄清误解，并在课堂上指导学生解决问题。这个基于手绘草图的系统将专注于学习基本的工程概念，而不是如何使用软件工具。这些工程概念与各种各样的设计直接相关，包括桥梁、建筑和桁架，这些对国家城市的基础设施至关重要。该项目将帮助培养能力更强的工程师来开发这些在社会上必不可少的设计。该项目旨在展示基于草图识别的辅导系统对学生动机和学习结果的影响，无论是在一般情况下还是在不同背景的学生中。MECHANIX系统将转换为HTML5格式，以便在所有设备上运行，并扩大其可访问性，供有各种技术要求的机构使用。将开发更多的人工智能算法，以适应更多类型的静力学问题，提高草图识别的准确性和速度，并改进教师的反馈机制，将学生在课堂上的表现信息合并在一起。升级后的系统将在五所不同大学的各种工程课程中进行学习，并向超过2500名工程及相关领域的学生介绍。研究人员将利用受控课堂实验、数字数据收集、概念前后测试、焦点小组和访谈来探索机械学习作为一种学习工具的外部有效性。协方差分析将被用来比较使用机械的学生和对照组的学生的结果。项目成果和MECHNIX软件将通过项目网站、专业发展讲习班和出版物共享。