Collaborative Research: Large-Scale Research on Engineering Design Based on Big Learner Data Logged by a CAD Tool

协作研究：基于 CAD 工具记录的大学习者数据的大规模工程设计研究

• 批准号: 1348530
• 负责人: Charles Xie
• 金额: $ 99.99万
• 依托单位: Concord Consortium
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1348530&HistoricalAwards=false
• 关键词: Collaborative; Research; Large; Scale; Engineering

PARTICIPATING INSTITUTIONS: Concord Consortium (Lead)Purdue UniversityCORE AREA(s): STEM Learning/STEM Learning EnvironmentsPROJECT DESCRIPTION Practicing science is one of the most important goals of K-12 engineering education, which is now part of the Next Generation Science Standards. Although previous research suggests that engineering design is an effective pedagogical approach to promoting science learning, there are concerns about the "design-science gap" that fails science learning in design projects. This project is delving into large quantities of process data to systematically identify bottlenecks in design processes that pose difficulties for students to apply science. Large learner datasets are being collected from over 3,000 students in Indiana and Massachusetts through automatic, unobtrusive logging of student design processes enabled by a unique CAD tool that supports the design of energy-efficient buildings using thermodynamics and heat transfer concepts. Large data sets - consisting of fine-grained information of student actions, experimentation results, electronic notes, and design artifacts - are used to reconstruct the entire learning trajectory of each individual student. Powerful process analytics (e.g., time series analysis and association rule mining) are being developed and applied to reveal patterns and trends across student groups and knowledge domains. Through a combination of these large data sets with pre/post-tests and demographic data, this project is answering the following research questions: RQ1: What are the common patterns of student design behaviors and how are they associated with prior knowledge, project duration, design performance, learning outcomes, and demographic factors? RQ2: How do students deepen their understanding of science concepts involved in engineering design projects? RQ3: How often and deeply do students use scientific experimentation to make a design choice? This five-year project is starting with six small-scale studies in years 1&2 to calibrate the process analytics by comparing with classroom observations, expert evaluations, and student interviews. The process analytics will then validate the research methodology by using the Informed Design Teaching and Learning Matrix, based on a meta-analysis of literature.BROADER SIGNIFICANCE The scale of the project will allow for greater representation of student diversity that is not readily attainable in small-scale studies. The project is contributing to the emerging fields of educational data mining and learning analytics through researching one of the most complex STEM practices -- engineering design. Computer Aided Design data possess all four characteristics of big data defined by IBM. The big data have the potential to yield direct, measurable evidence of learning at a statistically significant scale. Automation is making this research approach highly scalable and automatic process analytics is paving the road for building adaptive and predictive software for teaching engineering design. As a by-product of this project, the redacted datasets will be freely available to any researcher who is interested in mining them.

参与机构：Concord Consortium（Lead）Purdue UniversityCore领域：STEM学习/STEM学习环境描述实践科学是K-12工程教育的最重要目标之一，该目标现在已成为下一代科学标准的一部分。尽管以前的研究表明，工程设计是促进科学学习的有效教学方法，但人们对“设计科学差距”的担忧使设计项目中的科学学习失败。该项目正在研究大量的过程数据，以系统地识别设计过程中的瓶颈，这些瓶颈构成了学生在应用科学方面的困难。大型学习者数据集是通过自动，不引人注目的学生设计过程来从印第安纳州和马萨诸塞州的3,000多名学生那里收集的，该过程由独特的CAD工具启用，该工具支持使用热力学和传热概念来支持节能建筑物的设计。大型数据集（包括学生行动，实验结果，电子笔记和设计工件的细粒度信息）用于重建每个学生的整个学习轨迹。强大的过程分析（例如，时间序列分析和关联规则挖掘）正在开发和应用，以揭示学生团体和知识领域的模式和趋势。通过将这些大数据集与预测试和人口统计数据结合在一起，该项目正在回答以下研究问题：RQ1：学生设计行为的共同模式以及它们与先验知识，项目持续时间，设计绩效，学习绩效，学习量像和人口统计学因素有何关系？ RQ2：学生如何加深对工程设计项目中涉及的科学概念的理解？ RQ3：学生使用科学实验做出设计选择的频率和深度？这个为期五年的项目是从1和2年的六个小规模研究开始，通过与课堂观察，专家评估和学生访谈进行比较来校准流程分析。然后，该过程分析将通过基于文献荟萃分析的知情设计教学和学习矩阵来验证研究方法。Broader的重要性该项目的规模将允许在小规模研究中更加易于实现学生多样性。该项目通过研究最复杂的STEM实践之一 - 工程设计，为教育数据挖掘和学习分析的新兴领域做出了贡献。计算机辅助设计数据具有IBM定义的大数据的所有四个特征。大数据有可能在统计学意义上产生直接的，可衡量的学习证据。自动化正在使这种研究方法高度可扩展，并且自动过程分析正在为建立教学工程设计的自适应和预测软件铺平道路。作为该项目的副产品，任何有兴趣挖掘它们的研究人员都可以免费获得编辑后的数据集。

项目成果

Profiling self-regulation behaviors in STEM learning of engineering design

工程设计 STEM 学习中自我调节行为的剖析

• DOI: 10.1016/j.compedu.2019.103669
• 发表时间: 2020
• 期刊: Computers & Education
• 影响因子: 12
• 作者: Zheng, Juan;Xing, Wanli;Zhu, Gaoxia;Chen, Guanhua;Zhao, Henglv;Xie, Charles
• 通讯作者: Xie, Charles

A CAD-Based Research Platform for Data-Driven Design Thinking Studies

基于 CAD 的数据驱动设计思维研究平台

• DOI: 10.1115/1.4044395
• 发表时间: 2019
• 期刊: Journal of Mechanical Design
• 影响因子: 3.3
• 作者: Rahman, Molla;Schimpf, Corey;Xie, Charles;Sha, Zhenghui
• 通讯作者: Sha, Zhenghui