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1. First-Year Engineering Students’ Understanding and Application of Models: Comparing Impact of CATIA vs. MATLAB Courses

   Shah, N ; Thaker, P ; Rodgers, K ; Thompson, A ; Verleger, M ; Marbouti, F ( April 2021 , ASEE Southeastern Conference)

   null (Ed.)

   To succeed in engineering careers, students must be able to create and apply models to certain problems. The different types of modeling skills include physical, mathematical, computational, graphing, and financial. However, many students struggle to define and form relevant models in their engineering courses. We are hoping that the students are able to better define and apply models in their engineering courses after they have completed the MATLAB and/or CATIA courses. We also are hoping to see a difference in model identification between the MATLAB and CATIA courses. All students in the MATLAB and CATIA courses must be able to understand and create models in order to solve problems and think critically in engineering. Students need foundational knowledge about basic modeling skills that will be effective in their course. The goal is for students to create an approach to help them solve problems logically and apply different modeling skills. 

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2. First-year engineering students’ identification of models in engineering

   Shah, Nishith ; Thaker, Pujan ; Rodgers, Kelsey J. ; Verleger, Matthew A. ( April 2020 , Discovery Day presented by The Office of Undergraduate Research at Embry-Riddle Aeronautical University)

   Background To succeed in engineering careers, students must be able to create and apply models to certain problems. The different types of models include physical, mathematical, computational, graphical, and financial, which are used both in academics, research, and industry. However, many students struggle to define, create, and apply relevant models in their engineering courses. Purpose (Research Questions) The research questions investigated in this study are: (1) What types of models do engineering students identify before and after completing a first-year engineering course? (2) How do students’ responses compare across different courses (a graphical communications course - EGR 120 and a programming course - EGR 115), and sections? Design/Methods The data used for this study were collected in two introductory first-year engineering courses offered during Fall 2019, EGR 115 and EGR 120. Students’ responses to a survey about modeling were qualitatively analyzed. The survey was given at the beginning and the end of the courses. The data analyzed consisted of 560 pre and post surveys for EGR 115 and 384 pre and post surveys for EGR 120. Results Once the analysis is complete, we are hoping to find that the students can better define and apply models in their engineering courses after they have completed the EGR 115 and/or EGR 120 courses. 

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3. Change in student understanding of modeling during first-year engineering courses

   https://doi.org/10.18260/1-2--34269  

   Marbouti, Farshid ; Rodgers, Kelsey J. ; Verleger, Matthew A. ( June 2020 , ASEE Annual Conference proceedings)

   All engineers must be able to apply and create models to be effective problem solvers, critical thinkers, and innovative designers. To be more successful in their studies and careers, students need a foundational knowledge about models. An adaptable approach can help students develop their modeling skills across a variety of modeling types, including physical models, mathematical models, logical models, and computational models. Physical models (e.g., prototypes) are the most common type of models that engineering students identify and discuss during the design process. There is a need to explicitly focus on varying types of models, model application, and model development in the engineering curriculum, especially on mathematical and computational models. This NSF project proposes two approaches to creating a holistic modeling environment for learning at two universities. These universities require different levels of revision to the existing first-year engineering courses or programs. The proposed approaches change to a unified language and discussion around modeling with the intent of contextualizing modeling as a fundamental tool within engineering. To evaluate student learning on modeling in engineering, we conducted pre and post surveys across three different first-year engineering courses at these two universities with different student demographics. The comparison between the pre and post surveys highlighted student learning on engineering modeling based on different teaching and curriculum change approaches. 

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4. Modeling 3D Plants: Student Achievement Through Productive Failure

   Arango-Caro, S ; Langewisch, T ; Ying, K ; Arellano, M ; Callis-Duehl, K. ( October 2023 , Society for the Advancement of Biology Education Research (SABER) Midwest)

   To be successful in their future careers, students must be able to process information, devise creative solutions, and apply previous knowledge to new situations. Learning through only traditional teaching practices that rely heavily on lecture format and memorization is insufficient to prepare students for the future. Interactive project-based learning that experiences productive failure provides the opportunity for students to problem-solve novel topics and potentially fail at finding the solution. Through explanation, elaboration, comparison of iterations, refinement, and implementations, students can be more prepared to solve future problems. Our study examined the benefits of productive failure on high school students from both formal and informal learning environments working in collaborative teams to design and create 3D plant models. This STEAM project integrates science, design, and technology through innovative learning experiences in plant and agricultural science using emergent technologies. This learning experience encourages students to work together in collaborative teams of self-identified science, technophile, and art students to create 3D models of plants used in research at the Donald Danforth Plant Science Center in St. Louis, MO. Students learn about scientific research, the importance of plants in our society, and practice science communication skills. To create the 3D models, students must learn-by-doing to become proficient in using previously unfamiliar 3D modeling software where their teachers are merely facilitators. Students become active participants in their own learning by overcoming challenges through research, troubleshooting, teamwork, and perseverance. We used a mixed-method assessment approach comparing pre- and post-reflection questions. Students experience many challenges with learning the 3D model programs. They reported that they overcame difficulties working with the 3D modeling programs primarily through help from others and consulting outside resources, such as YouTube videos, as well as through continued effort. Students indicated that they faced challenges when creating their models but recognized that this project was a learning experience. Productive failure through the process of struggling and learning from one’s mistakes can encourage positive learning outcomes and give students a better ability to overcome future challenges. 

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5. Comparing students’ solutions to an open-ended problem in an introductory programming course with and without explicit modeling interventions

   https://doi.org/10.18260/1-2--34311  

   Rodgers, Kelsey J. ; Verleger, Matthew A. ; Marbouti, Farshid ( June 2020 , ASEE Annual Conference proceedings)

   Engineers must understand how to build, apply, and adapt various types of models in order to be successful. Throughout undergraduate engineering education, modeling is fundamental for many core concepts, though it is rarely explicitly taught. There are many benefits to explicitly teaching modeling, particularly in the first years of an engineering program. The research questions that drove this study are: (1) How do students’ solutions to a complex, open-ended problem (both written and coded solutions) develop over the course of multiple submissions? and (2) How do these developments compare across groups of students that did and did not participate in a course centered around modeling?. Students’ solutions to an open-ended problem across multiple sections of an introductory programming course were explored. These sections were all divided across two groups: (1) experimental group - these sections discussed and utilized mathematical and computational models explicitly throughout the course, and (2) comparison group - these sections focused on developing algorithms and writing code with a more traditional approach. All sections required students to complete a common open-ended problem that consisted of two versions of the problem (the first version with smaller data set and the other a larger data set). Each version had two submissions – (1) a mathematical model or algorithm (i.e. students’ written solution potentially with tables and figures) and (2) a computational model or program (i.e. students’ MATLAB code). The students’ solutions were graded by student graders after completing two required training sessions that consisted of assessing multiple sample student solutions using the rubrics to ensure consistency across grading. The resulting assessments of students’ works based on the rubrics were analyzed to identify patterns students’ submissions and comparisons across sections. The results identified differences existing in the mathematical and computational model development between students from the experimental and comparison groups. The students in the experimental group were able to better address the complexity of the problem. Most groups demonstrated similar levels and types of change across the submissions for the other dimensions related to the purpose of model components, addressing the users’ anticipated needs, and communicating their solutions. These findings help inform other researchers and instructors how to help students develop mathematical and computational modeling skills, especially in a programming course. This work is part of a larger NSF study about the impact of varying levels of modeling interventions related to different types of models on students’ awareness of different types of models and their applications, as well as their ability to apply and develop different types of models. 

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