Award Abstract # 2105695
Collaborative Research: SmartCAD: Guiding Engineering Design with Science Simulations

NSF Org: DRL
Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient: INSTITUTE FOR FUTURE INTELLIGENCE INC
Initial Amendment Date: December 18, 2020
Latest Amendment Date: December 18, 2020
Award Number: 2105695
Award Instrument: Continuing Grant
Program Manager: Robert Ochsendorf
rochsend@nsf.gov
 (703)292-2760
DRL
 Division of Research on Learning in Formal and Informal Settings (DRL)
EDU
 Directorate for STEM Education
Start Date: December 15, 2020
End Date: May 31, 2022 (Estimated)
Total Intended Award Amount: $2,192,610.00
Total Awarded Amount to Date: $531,239.00
Funds Obligated to Date: FY 2017 = $531,239.00
History of Investigator:
  • Charles Xie (Principal Investigator)
    charles@intofuture.org
Recipient Sponsored Research Office: INSTITUTE FOR FUTURE INTELLIGENCE, INC.
26 ROCKLAND ST
NATICK
MA  US  01760-5852
(508)397-7021
Sponsor Congressional District: 05
Primary Place of Performance: Institute for Future Intelligence
26 Rockland Street
Natick
MA  US  01760-5852
Primary Place of Performance
Congressional District:
05
Unique Entity Identifier (UEI): T8LKNJC8D5R3
Parent UEI:
NSF Program(s): Discovery Research K-12
Primary Program Source: 04001718DB NSF Education & Human Resource
Program Reference Code(s):
Program Element Code(s): 764500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.

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 computer-aided design (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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 20)
Li, S., Du, H., Xing, W., Zheng, J., Chen, G., & Xie, C. "Examining Temporal Dynamics of Self-Regulated Learning Behaviors in STEM Learning: A Network Approach" Computers & Education , 2020 10.1016/j.compedu.2020.103987
Li, Shan and Zheng, Juan and Huang, Xudong and Xie, Charles "Self-regulated learning as a complex dynamical system: Examining students' STEM learning in a simulation environment" Learning and Individual Differences , v.95 , 2022 https://doi.org/10.1016/j.lindif.2022.102144 Citation Details
Li, S., Zheng, J., Huang, X., & Xie, C. "Self-Regulated Learning as a Complex Dynamical System: Examining Students STEM Learning in a Simulation Environment" Learning and Individual Differences , v.95 , 2022 , p.102144 10.1016/j.lindif.2022.102144
Magana, A.J., Chiu, J., Seah, Y.Y., Bywater, J., Schimpf, C., Karabiyik, T., Rebello, S., & Xie, C. "Classroom Orchestration of Computer Simulations for Science and Engineering Learning: A Cross-Case Study Approach" International Journal of Science Education , 2021 10.1080/09500693.2021.1902589
Magana, Alejandra J. and Chiu, Jennifer and Ying Seah, Ying and Bywater, James P. and Schimpf, Corey and Karabiyik, Tugba and Rebello, Sanjay and Xie, Charles "Classroom orchestration of computer simulations for science and engineering learning: a multiple-case study approach" International Journal of Science Education , 2021 https://doi.org/10.1080/09500693.2021.1902589 Citation Details
Purzer, S. and Schimpf, C. and Quintana-Cifuentes, J. and Sereiviene, E. and Lingam, I and Jiang, R. "Refine By Design: An Engineering Design Coaching Tool for Supporting Student Reasoning" The science teacher , v.89 , 2022 Citation Details
Rahman, M. H., Xie, C., & Sha, Z. "Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors" Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 17-2, 2021, Online, Virtual , 2021
Rahman, M. H., Xie, C., & Sha, Z. "Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks" Journal of Mechanical Design , 2021 10.1115/1.4049971
Rahman, Molla Hafizur and Xie, Charles and Sha, Zhenghui "Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks" Journal of Mechanical Design , v.143 , 2021 https://doi.org/10.1115/1.4049971 Citation Details
Sereiviene, Elena and Ding, Xiaotong and Jiang, Rundong and Bulseco, Dylan and Xie, Charles "Learning Science and Engineering by Designing Sustainable Houses" The Science Teacher , v.92 , 2025 https://doi.org/10.1080/00368555.2025.2469120 Citation Details
Sereiviene, Elena and Ding, Xiaotong and Jiang, Rundong and Zheng, Juan and Kashyrskyy, Andriy and Bulseco, Dylan and Xie, Charles "Introducing Engineering Design to First-Year Students Through the Net Zero Energy Challenge" Journal of College Science Teaching , 2024 https://doi.org/10.1080/0047231X.2024.2380302 Citation Details
(Showing: 1 - 10 of 20)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

A common need in project-based engineering education is to provide students timely feedback while they are solving design problems. But it is unrealistic to expect teachers to monitor and mentor each individual student in the classroom all the time. The lack of personalized formative feedback in design processes limits students' ability to iterate through many possibilities to find optimal solutions, undercutting the promise of engineering education in teaching students to become creative designers. This project investigated how science simulations can be embedded in engineering design tools to guide students in solving design challenges in real time. To support the research, the project has developed an open-source, Web-based CAD software tool named as Aladdin (https://intofuture.org/aladdin.html), which supports architectural engineering and renewable energy engineering. With Aladdin, students can draw a 3D model, set the properties of each element of the model, and then analyze its performance -- all within a single system. Based on the analysis results, students can then decide their next steps. Furthermore, using artificial intelligence, Aladdin can provide students with automatic assessment of their designs by evaluating the extent to which a design needs to be improved. Based on Aladdin, the project has conducted empirical research in collaboration with middle and high schools in multiple states, involving hundreds of students from diverse socioeconomic backgrounds. Pre/post-test results show learning gains in science concepts. Learning analytics was used to reveal self-regulation behaviors of students from the process data collected by the software through even logging that captures students' fine-grained interactions with the software.


Last Modified: 08/16/2022
Modified by: Charles Xie

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