Award Abstract # 1725659
Enhancing Visualization Skills and Conceptual Understanding Using a Drawing-Recognition Tutoring System for Engineering Students

NSF Org: DUE
Division Of Undergraduate Education
Recipient: LETOURNEAU UNIVERSITY
Initial Amendment Date: July 21, 2017
Latest Amendment Date: July 21, 2017
Award Number: 1725659
Award Instrument: Standard Grant
Program Manager: Christine Delahanty
cdelahan@nsf.gov
 (703)292-8492
DUE
 Division Of Undergraduate Education
EDU
 Directorate for STEM Education
Start Date: September 1, 2017
End Date: August 31, 2023 (Estimated)
Total Intended Award Amount: $137,420.00
Total Awarded Amount to Date: $137,420.00
Funds Obligated to Date: FY 2017 = $137,420.00
History of Investigator:
  • Benjamin Caldwell (Principal Investigator)
    BenjaminCaldwell@letu.edu
Recipient Sponsored Research Office: LeTourneau University
2100 S MOBBERLY AVE
LONGVIEW
TX  US  75602-3564
(903)233-3100
Sponsor Congressional District: 01
Primary Place of Performance: LeTourneau University
TX  US  75607-7001
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): GKEEPUL16D56
Parent UEI: GKEEPUL16D56
NSF Program(s): IUSE
Primary Program Source: 04001718DB NSF Education & Human Resource
Program Reference Code(s): 8209, 8244, 9178
Program Element Code(s): 199800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Runyon, Matthew and Talley, Kimberly G. and Viswanathan, Vimal K. and Shryock, Kristi J. and Caldwell, Benjamin and Linsey, Julie S. and Hammond, Tracy A. "Changing Homework Achievement with Mechanix Pedagogy: A Recap" ASEE 2022 Annual Conference , 2022 Citation Details
Viswanathan, V and Hurt, J and Hammond, T and Caldwell, B and Talley, K and Linsey, J "A Study on the Impact of a Sketch-based Tutoring System in Statics Instruction" Proceedings of the 2020 ASEE Annual Conference , 2020 Citation Details

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.

When engineers design systems like bridges or machines, they must ensure that the system and parts are strong enough to withstand the loads acting on it. Engineers often use a "free-body diagram" (FBD) to help with this design and analysis process. A FBD is a sketch of the system and the loads acting on it. It is important to teach engineering students how to draw and analyze a FBD correctly, but this is a difficult concept, can require a lot of practice, and often requires one-on-one feedback or grading from instructors. Before this research project, the only way to grade a FBD was manually by an instructor or other highly trained individual. Computers have not been able to perform this complex task because FBDs involve sketching, making correct assumptions about objects and the loads they experience, writing complex equations, and solving the equations. The time and effort required to provide one-on-one feedback is challenging for instructors, especially for large classes with hundreds of students.

Researchers on this project had previously developed software called Mechanix and shown how, using Artificial Intelligence (AI), it could recognize and understand a student's sketch, determine if it accurately represented the system, and if the student is evaluating the system correctly. This prior research showed that Mechanix had the potential to help students learn faster and more effectively by instantly giving them customized feedback. The goal of this new research project was to develop the additional complex algorithms and code necessary to accurately recognize and evaluate additional types of FBDs, to build Mechanix software into a complete online homework system, and to determine the impact of Mechanix on student learning. Researchers at Texas A&M University, Georgia Institute of Technology, Texas State University, San Jose State University, and LeTourneau University worked together to accomplish these goals.

The AI algorithms were successfully developed, tested, and built into a fully updated and rewritten version of Mechanix software. Instructors using the online platform can create homework assignments and view students' performance. Students can complete assignments by sketching a FBD, drawing appropriate forces, and writing equations necessary to evaluate the FBD. Mechanix is able to check student work for accuracy at various points in the process and can provide specific feedback instantly. Throughout this research project, trials of Mechanix were conducted in classes at all five universities with more than 1,800 students involved, and data on its effectiveness was carefully collected. Analysis of the data shows that the online Mechanix homework system is as effective as traditional, tried-and-true methods of evaluating FBDs and provides the feedback significantly faster. Because of the quick feedback, Mechanix also supports exploratory learning through creative engineering design problems where students can design bridge trusses to meet specific standards. These findings helped advance the fields of computer science and engineering education and resulted in many scholarly research papers and presentations in these fields.

LeTourneau University's role in this research project was using Mechanix in multiple engineering classes and collecting student performance data, such as homework scores and test scores. The data collected from students using Mechanix was compared to students who used a traditional homework system that included paper-and-pencil assignments and manual grading to understand the impact of Mechanix on student learning. Data from LeTourneau University was also compared to data from other institutions to understand if the impact was consistent at different universities, in different courses, and for different types of students. LeTourneau University also developed a bank of more than one hundred homework problems and solutions that could be used by instructors and students in this homework system.

Overall, this research shows that Mechanix is a helpful tool in the classroom to help reduce the burden of grading while providing feedback on student free body diagrams. Instructors using this system are able to provide students with feedback based upon scaffolded learning theories and have their homework assignments automatically graded for approximately the same amount of effort needed to set up a homework assignment in any online homework system. Further, Mechanix allows instructors to assign and grade open-ended problems, which would otherwise require a significant amount of grading time due to the possibility of each submission being unique. Students and instructors both seem to enjoy using Mechanix and students perform at least as well on Mechanix as other homework systems with the added requirement of drawing their free body diagrams.


Last Modified: 12/19/2023
Modified by: Benjamin W Caldwell

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