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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. 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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4. Comparing Students’ Solutions to an Open-ended Problem in an Introductory Programming Course with and without Explicit Modeling Interventions

   Rodgers, K ; Verleger, M ; Marbouti, F ( January 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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5. Social Dialogue in the Engineering Classroom: The Effect of National Events on the Political and Social Attitudes of First-Year Engineering Students

   Langus, Tara C. ; Pearson, Nelson ; Major, Justin ; Rodríguez-Simmonds, Héctor E. ; Godwin, Allison ; Kirn, Adam ( January 2018 , ASEE annual conference & exposition)

   This research paper focuses on the effect of recent national events on first-year engineering students’ attitudes about their political identity, social welfare, perspectives of diversity, and approaches to social situations. Engineering classrooms and cultures often focus on mastery of content and technical expertise with little prioritization given to integrating social issues into engineering. This depoliticization (i.e., the removal of social issues) in engineering removes the importance of issues related to including diverse individuals in engineering, working in diverse teams, and developing cultural sensitivity. This study resulted from the shift in the national discourse, during the 2016 presidential election, around diversity and identities in and out of the academy. We were collecting interview data as a part of a larger study on students attitudes about diversity in teams. Because these national events could affect students’ perceptions of our research topic, we changed a portion of our interviews to discuss national events in science, technology, engineering, and mathematics (STEM) classrooms and how students viewed these events in relation to engineering. We interviewed first-year undergraduate students (n = 12) who indicated large differences of attitudes towards diverse individuals, experiences with diverse team members, and/or residing at the intersection of multiple diversity markers. We asked participants during the Spring of 2017 to reflect on the personal impact of recent national events and how political discussions have or have not been integrated into their STEM classrooms. During interviews students were asked: 1) Have recent national events impacted you in any way? 2) Have national events been discussed in your STEM classes? 3) If so, what was discussed and how was it discussed? 4) Do these conversations have a place in STEM classes? 5) Are there events you wish were discussed that have not been? Inductive coding was used to analyze interviews and develop themes that were audited for quality by the author team. Two preliminary themes emerged from analysis: political awareness and future-self impact. Students expressed awareness of current political events at the local, national and global levels. They recognized personal and social impacts that these events imposed on close friends, family members, and society. However, students were unsure of how to interpret political dialogue as it relates to policy in engineering disciplines and practices. This uncertainty led students to question their future-selves or careers in engineering. As participants continued to discuss their uncertainty, they expressed a desire to make explicit connections between politics and STEM and their eventual careers in STEM. These findings suggest that depoliticization in the classroom results in engineering students having limited consciousness of how political issues are relevant to their field. This disconnect of political discourse in the classroom gives us a better understanding of how engineering students make sense of current national events in the face of depoliticization. By re-politicising STEM classrooms in a way relevant to students’ futures, educators can better utilize important dialogues to help students understand how their role as engineers influence society and how the experiences of society can influence their practice of engineering. 

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