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1. Types of Models Identified by First-Year Engineering Students

   Rodgers, Kelsey J. ; Thompson, Angela ; Verleger, Matthew A. ; Marbouti, Farshid ; Shah, Nishith ; Thaker, Pujan ( July 2021 , Zone 1 Conference of the American Society for Engineering Education)

   null (Ed.)

   Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students’ understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students’ responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students’ responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students’ understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students’ awareness of models and their ability to build and apply models. 

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2. Developing a Program to Assist in Qualitative Data Analysis: How Engineering Students’ Discuss Model Types

   Rodgers K ; Thompson A ; Hawkins N ; Verleger M ; Marbouti F ( January 2022 , 2022 ASEE Annual Conference)

   This Research paper discusses the opportunities that utilizing a computer program can present in analyzing large amounts of qualitative data collected through a survey tool. When working with longitudinal qualitative data, there are many challenges that researchers face. The coding scheme may evolve over time requiring re-coding of early data. There may be long periods of time between data analysis. Typically, multiple researchers will participate in the coding, but this may introduce bias or inconsistencies. Ideally the same researchers would be analyzing the data, but often there is some turnover in the team, particularly when students assist with the coding. Computer programs can enable automated or semi-automated coding helping to reduce errors and inconsistencies in the coded data. In this study, a modeling survey was developed to assess student awareness of model types and administered in four first-year engineering courses across the three universities over the span of three years. The data collected from this survey consists of over 4,000 students’ open-ended responses to three questions about types of models in science, technology, engineering, and mathematics (STEM) fields. A coding scheme was developed to identify and categorize model types in student responses. Over two years, two undergraduate researchers analyzed a total of 1,829 students’ survey responses after ensuring intercoder reliability was greater than 80% for each model category. However, with much data remaining to be coded, the research team developed a MATLAB program to automatically implement the coding scheme and identify the types of models students discussed in their responses. MATLAB coded results were compared to human-coded results (n = 1,829) to assess reliability; results matched between 81%-99% for the different model categories. Furthermore, the reliability of the MATLAB coded results are within the range of the interrater reliability measured between the 2 undergraduate researchers (86-100% for the five model categories). With good reliability of the program, all 4,358 survey responses were coded; results showing the number and types of models identified by students are presented in the paper. 

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3. 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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4. How Do Student Perceptions of Engineers and Engineering as a Career Relate to Their Self-Efficacy, Career Expectations, and Grittiness?

   Morris, Melissa Lynn ; Dygert, Joseph ; Hensel, Robin A. ( June 2020 , 2020 ASEE Virtual Annual Conference Content Access)

   This complete research paper examines the connection between student beliefs about engineering as a profession, as well as the perceptions of their family and friends, to their reported self-efficacy, career expectations, and grittiness. The student responses examined were obtained from non-calculus ready engineering students at a large land grant institution in the Mid-Atlantic region. The students participated in a well-established program focused on cohort formation, mentorship, professional skill development, and fostering a sense of inclusion and belonging in engineering. The program, consisting of a one-week pre-fall bridge experience and two common courses, was founded in 2012 and has been operating with National Science Foundation (NSF) S-STEM funding since 2016. Students who received S-STEM funded scholarships are required to participate in focus groups, one-on-one interviews, and complete LAESE, MSLQ, and GRIT questionnaires each semester. The researchers applied qualitative coding methods to evaluate student responses from focus groups and one-one-one interviews which were conducted from 2017 to 2019. Questions examined in this paper include: 1) How would you describe an engineer? 2) Please describe what you think an engineer does on a daily basis. 3) What do you think your friends/family think of engineering? 4) What skills or characteristics do you think good engineers have? 5) What types of careers do you believe are filled by degree holding engineers? Student responses on the aforementioned questions were related to the self-efficacy, career expectation, and grit values obtained from the LAESE, MSLQ, and GRIT instruments. The nature of this longitudinal study allows the evolution of student responses to also be examined as they matriculate through their education. Additional analysis was performed to identify themes and numerical trends associated with student populations such as, underrepresented minorities, females, and first-generation college students. Results of this research are presented in an effort to further highlight the importance of exposure to STEM fields during an individual’s K-12 education, and express how student perceptions, self-efficacy, GRIT, and career expectations evolve over their undergraduate education. 

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5. 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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