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How First-Year Engineering Students Develop Visualizations for Mathematical Models

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

First-Year Programs: Tuesday Potpourri

Tagged Division

First-Year Programs

Page Count

19

Permanent URL

https://peer.asee.org/28446

Download Count

90

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Paper Authors

biography

Kelsey Joy Rodgers Embry-Riddle Aeronautical University, Daytona Beach

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Kelsey Rodgers is an assistant professor in the Engineering Fundamentals Department at Embry-Riddle Aeronautical University. She teaches a MATLAB programming course to mostly first-year engineering students. She primarily investigates how students develop mathematical models and simulations and effective feedback. She graduated from the School of Engineering Education at Purdue University with a doctorate in engineering education. She previous conducted research in Purdue University's First-Year Engineering Program with the Network for Nanotechnology (NCN) Educational Research team, the Model-Eliciting Activities (MEAs) Educational Research team, and a few fellow STEM education graduates for an obtained Discovery, Engagement, and Learning (DEAL) grant. Prior to attending Purdue University, she graduated from Arizona State University with her B.S.E. in Engineering from the College of Technology and Innovation, where she worked on a team conducting research on how students learn LabVIEW through Disassemble, Analyze, Assemble (DAA) activities.

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Nanmwa Jeremiah Dala Embry-Riddle Aeronautical University

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Jeremiah is a senior at Embry-Riddle Aeronautical University majoring in Aerospace Engineering and Computational Mathematics. He is currently conducting research on How First-Year Engineering Students Develop Visualizations for Mathematical Models with Professor Kelsey Rodgers.

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Krishna Madhavan Purdue University, West Lafayette (College of Engineering)

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Dr. Krishna Madhavan is an Associate Professor in the School of Engineering Education. In 2008 he was awarded an NSF CAREER award for learner-centric, adaptive cyber-tools and cyber-environments using learning analytics. He leads a major NSF-funded project called Deep Insights Anytime, Anywhere (http://www.dia2.org) to characterize the impact of NSF and other federal investments in the area of STEM education. He also serves as co-PI for the Network for Computational Nanotechnology (nanoHUB.org) that serves hundreds of thousands of researchers and learners worldwide. Dr. Madhavan served as a Visiting Researcher at Microsoft Research (Redmond) focusing on big data analytics using large-scale cloud environments and search engines. His work on big data and learning analytics is also supported by industry partners such as The Boeing Company. He interacts regularly with many startups and large industrial partners on big data and visual analytics problems. He was one of 49 faculty members selected as the nation’s top engineering educators and researchers by the U.S. National Academy of Engineering to the Frontiers in Engineering Education symposium.

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Abstract

This research paper presents new findings about first-year engineering students’ approaches for visualizing models within the Models and Modeling Perspective theoretical framework. The results are primarily discussed in the full paper, but the motivation, background, implications, and future directions for research are briefly described in this abstract.

The development and use of mathematical models and simulations underlies much of the work of engineers. Mathematical models describe a situation or system through mathematics, quantification, and pattern identification. Simulations enable users to interact with models through manipulation of input variables and visualization of model outputs. Although modeling skills are fundamental, they are rarely explicitly taught in engineering. Since models are embedded in many engineering courses, it is beneficial to help students develop modeling skills early on in their engineering education. Model-eliciting activities (MEAs) represent a pedagogical approach implemented and researched in engineering to teach students mathematical modeling skills through the development of a model to solve an authentic problem. Model-adaptation activities (MAAs) were created within the same theoretical framework in mathematics education, but they are scarcely implemented and researched within engineering.

This study is a part of a larger investigation into the impact of implementing a linked MEA and MAA within a first-year engineering course at a large Midwestern university. The purpose of this research is to further address the need for developing effective curricula to teach students’ mathematical modeling skills and begin to address the need to teach students about simulations.

The data for this study started with 122 student teams’ submissions at the end of a MEA and a MAA. Within the MEA, teams had to develop one mathematical model to create a solar panel with various materials based on provided data and some relevant equations. Within the MAA, teams had to develop one simulation per a student on the team based on the model from their MEA and other models they found on nanoHUB.org or other resources. The teams developed their simulations with graphical-user interfaces using MATALB. The nature of the mathematical models was analyzed in a previous study and the relevant findings are tied throughout this study. The teams’ simulations submitted at the end of the MAA were analyzed using a framework to assess the level of simulation completeness based on the presence of interaction, an underlying model, and visualization. Based on an analysis of the 122 teams’ 383 simulations, 62 percent were complete simulations (i.e. backed by a model and front-ended with user-input and output visualization capabilities). All of the 237 complete simulations were further analyzed using grounded theory in this study to understand the types of visualizations that students used and how they related to their underlying mathematical models. There was a large range of ways that students decided to incorporate visualization into their simulations. Most teams visualized an output of their model, but some chose to display values that users input into the models. The most common types of visualization consisted of bar charts, pie charts, and line graphs. Some teams implemented 3-D objects, but these appeared to be beyond the capability of students because they did not display any meaningful information. These findings are described quantitatively and qualitatively in greater detail in the full paper.

The goal of this study was to gain further insight into students’ thought-process of the meaning of their models. This understanding can help researchers better investigate potential misconceptions, misunderstandings, and opportunities to help students learn about mathematical models. The findings of this study also help inform practioners of ineffective and effective types of visualizations that students used in developing simulations to help them give more informed feedback throughout implementation of similar projects.

Rodgers, K. J., & Dala, N. J., & Madhavan, K. (2017, June), How First-Year Engineering Students Develop Visualizations for Mathematical Models Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/28446

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