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  1. 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. 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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  3. 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. 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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  5. 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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  6. This complete research paper describes the impact of a modeling intervention on first-year engineering students’ modeling skills in an introductory computer programming course. Five sections of the first-year engineering introductory programming course at a private, STEM+Business institution were revised to center around modeling concepts. These five sections made up the experimental group for this study. The comparison group consisted of four sections of the course that were not revised. Students in all these sections were given two different versions of a modeling problem two times in the semester to test their progress in gaining modeling skills. Each version required two submissions – a written solution and a coded solution. The assessment of these four submissions based on the three established dimensions of modeling were quantitatively analyzed in this study. The three dimensions within mathematical modeling that were the focus of this study were mathematical model complexity, modifiability, and reusability. Mathematical model complexity is being able to address the complexity of the problem. Modifiability addresses the generalizability of the model solution. Reusability is showing an understanding of the problem and the user. Statistical analysis showed that students in the experimental group had more gains in their demonstrated modeling abilities across all three dimensions than the students in the comparison group. This study demonstrated that intentional and explicit instructional strategies targeting model development resulted in greater gains in students’ demonstrated modeling skills and both their written and coded solutions to a complex modeling problem. 
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  7. Abstract Background

    The concepts of size and scale in nanotechnology are difficult for most beginning engineering students to grasp. Yet, guidance on the specific aspects of size and scale that should be taught and assessed is limited.

    Purpose

    This research sought to empirically develop a framework for size and scale conceptualization and provide a blueprint to guide curriculum development and assessment.

    Design/Methods

    Through an exploratory sequential mixed methods design, we qualitatively examined 30 teams of 119 first‐year engineering students' nanotechnology‐based projects to identify concepts beyond those in the literature to create a Size and Scale Framework (SSF). We then created a blueprint with associated learning objectives that can guide curriculum and assessment development. To demonstrate the utility of the SSF blueprint, an SSF‐based quiz was developed and studied using classical test theory with 378 first‐year engineering students.

    Results

    The findings categorized size and scale in terms of eight aspects: Definition, Qualitative Categorical, Qualitative Relational, Qualitative Proportional, Quantitative Absolute, Quantitative Categorical, Quantitative Relational, and Quantitative Proportional. The SSF can be applied as a blueprint for others to develop curriculum and assessment. The SSF‐based quiz demonstrated acceptable properties for use with first‐year engineering students.

    Conclusions

    Development of the SSF‐based quiz is an example of how the SSF can be applied to create a classroom quiz to assess students' size and scale knowledge in the context of nanotechnology.

     
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