SmartCAD: Guiding Engineering Design with Science Simulations (Collaborative Research: Magana-de-Leon)

This project investigates how real time formative feedback can be automatically composed from the results of computational analysis of student design artifacts and processes with the envisioned SmartCAD software. The project conducts design-based research on SmartCAD, which supports secondary science and engineering with three embedded computational engines capable of simulating the mechanical, thermal, and solar performance of the built environment.

Full Description

In this project, SmartCAD: Guiding Engineering Design with Science Simulations, the Concord Consortium (lead), Purdue University, and the University of Virginia investigate how real time formative feedback can be automatically composed from the results of computational analysis of student design artifacts and processes with the envisioned SmartCAD software. Through automatic feedback based on visual analytic science simulations, SmartCAD is able to guide every student at a fine-grained level, allowing teachers to focus on high-level instruction. Considering the ubiquity of CAD software in the workplace and their diffusion into precollege classrooms, this research provides timely results that could motivate the development of an entire genre of CAD-based learning environments and materials to accelerate and scale up K-12 engineering education. The project conducts design-based research on SmartCAD, which supports secondary science and engineering with three embedded computational engines capable of simulating the mechanical, thermal, and solar performance of the built environment. These engines allow SmartCAD to analyze student design artifacts on a scientific basis and provide automatic formative feedback in forms such as numbers, graphs, and visualizations to guide student design processes on an ongoing basis. 

The research hypothesis is that appropriate applications of SmartCAD in the classroom results in three learning outcomes: 1) Science knowledge gains as indicated by a deeper understanding of the involved science concepts and their integration at the completion of a design project; 2) Design competency gains as indicated by the increase of iterations, informed design decisions, and systems thinking over time; and 3) Design performance improvements as indicated by a greater chance to succeed in designing a product that meets all the specifications within a given period of time. While measuring these learning outcomes, this project also probes two research questions: 1) What types of feedback from simulations to students are effective in helping them attain the outcomes? and 2) Under what conditions do these types of feedback help students attain the outcomes? To test the research hypothesis and answer the research questions, this project develops three curriculum modules based on the Learning by Design (LBD) Framework to support three selected design challenges: Solar Farms, Green Homes, and Quake-Proof Bridges. This integration of SmartCAD and LBD situate the research in the LBD context and shed light on how SmartCAD can be used to enhance established pedagogical models such as LBD. Research instruments include knowledge integration assessments, data mining, embedded assessments, classroom observations, participant interviews, and student questionnaires. This research is carried out in Indiana, Massachusetts, and Virginia simultaneously, involving more than 2,000 secondary students at a number of socioeconomically diverse schools. Professional development workshops are provided to familiarize teachers with SmartCAD materials and implementation strategies prior to the field tests. An external Critical Review Committee consisting of five engineering education researchers and practitioners oversee and evaluate this project formatively and summative. Project materials and results are disseminated through publications, presentations, partnerships, and the Internet.

PROJECT KEYWORDS

Project Materials

Title Type Post date Sort ascending
Comparing Optimization Practices Across Engineering Learning Contexts Using Process Data Resource 01/30/2024 - 03:47pm
Comparing Optimization Practices Across Engineering Learning Contexts Using Process Data Resource 01/30/2024 - 03:47pm
Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach Resource 07/22/2022 - 03:06pm
Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach Resource 07/22/2022 - 03:06pm
Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Multiple-Case Study Approach Resource 07/22/2022 - 03:06pm
Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design Resource 07/22/2022 - 02:48pm
Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design Resource 07/22/2022 - 02:48pm
Using Machine Learning to Predict Engineering Technology Students’ Success with Computer-Aided Design Resource 07/22/2022 - 02:48pm
Energy3D Resource 09/08/2021 - 03:46pm
Energy3D Resource 09/08/2021 - 03:46pm
Energy3D Resource 09/08/2021 - 03:46pm
Characterizing the Interplay of Cognitive and Metacognitive Knowledge in Computational Modeling and Simulation Practices Resource 06/13/2019 - 12:43pm
Exploring Students’ Experimentation Strategies in Engineering Design Using an Educational CAD Tool Resource 06/13/2019 - 12:36pm
The Role of Simulation-Enabled Design Learning Experiences on Middle School Students’ Self-generated Inherence Heuristics Resource 06/13/2019 - 11:50am
SmartCAD: Guiding Engineering Design with Science Simulations (Collaborative Research) Poster 06/06/2016 - 01:08pm
SmartCAD: Guiding Engineering Design with Science Simulations (Collaborative Research) Poster 06/06/2016 - 01:08pm
SmartCAD: Guiding Engineering Design with Science Simulations (Collaborative Research) Poster 06/06/2016 - 01:08pm