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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 recoding 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 semiautomated 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 firstyear engineering courses across the three universities over the span of three years. The data collected from this survey consists of over 4,000 students’ openended 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 amore »

This is a Complete Research paper. Understanding models is important for engineering students, but not often taught explicitly in firstyear 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 firstyear engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to firstyear 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 iterativemore »

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.

Previous work identified an anthropogenic fingerprint pattern in 𝑇AC (𝑥, 𝑡), the amplitude of the seasonal cycle of mid to upper tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite 𝑇AC(𝑥,𝑡) data could have been influenced by realworld multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their 𝑇AC (𝑥, 𝑡) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 34) intermodel and interrealization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, globalscale fingerprint patterns are spatially dissimilar to the smallerscale patterns of internal 𝑇AC(𝑥,𝑡) variability associated with the Atlantic Multidecadal Oscillation and the El Niño~Southernmore »

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, openended 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 openended 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 openended 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 hadmore »