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  1. Abstract Background

    A predictor of student success, sense of belonging (SB) is often inhibited for minoritized students in engineering environments and difficult to foster in online courses. A shift to remote learning formats necessitated by COVID‐19, therefore, posed an additive threat to SB for engineering first‐year students, especially those with minoritized identities. Research is needed to understand impacts of online learning to SB for engineering students.

    Purpose Hypothesis(es)

    The study examined factors that promoted or detracted from SB in engineering in remote courses and ways in which identity related to SB.

    Design Method

    Part of a larger mixed‐methods study, this article examines focus group data from 31 first‐year engineering students in 2020 to characterize student experiences in engineering courses moved online during COVID‐19.

    Results

    In addition to the mutually reinforcing nature of SB and learning, findings reveal that the major factors of (a) peer interactions, (b) instructor behavior and course design, (c) environmental identity cues, and (d) personal and psychological factors influenced SB. Examples of factors that positively contributed to SB in remote‐delivery courses included platforms for open communication with peers, “live” ability to ask complex questions, and a critical mass of peers of similar identity; example factors hindering SB included limited use ofmore »cameras in synchronous classes, elitist peer interactions, instructor focus on academic performance (vs. growth), and feelings of self‐doubt.

    Conclusions

    Both identity and COVID‐19 impacted SB for students, with results showing four pathways to support SB and learning for diverse students in engineering across course formats.

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  2. 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 amore »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.« less
  3. This is a Complete Research paper. 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 iterativemore »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.« less
  4. 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.
  5. The stability of the West Antarctic Ice Sheet (WAIS) depends on ocean heat transport toward its base and remains a source of uncertainty in sea level rise prediction. The Antarctic Slope Current (ASC), a major boundary current of the ocean's global circulation, serves as a dynamic gateway for heat transport toward Antarctica. Here, we use observations collected from the Bellingshausen Sea to propose a mechanistic explanation for the initiation of the westward-flowing ASC. Waters modified throughout the Bellingshausen Sea by ocean-sea-ice and ocean-ice-shelf interactions are exported to the continental slope in a narrow, topographically steered western boundary current. This focused outflow produces a localized front at the shelf break that supports the emerging ASC. This mechanism emphasizes the importance of buoyancy forcing, integrated over the continental shelf, as opposed to local wind forcing, in the generation mechanism and suggests the potential for remote control of melt rates of WAIS' largest ice shelves.
  6. Abstract

    Antarctic Bottom Water (AABW), which fills the global ocean abyss, is derived from dense water that forms in several distinct Antarctic shelf regions. Previous modeling studies have reached conflicting conclusions regarding export pathways of AABW across the Southern Ocean and the degree to which AABW originating from distinct source regions are blended during their export. This study addresses these questions using passive tracer deployments in a 61‐year global high‐resolution (0.1°) ocean/sea‐ice simulation. Two distinct export “conduits” are identified: Weddell Sea‐ and Prydz Bay‐sourced AABW are blended together and exported mainly to the Atlantic and Indian Oceans, while Ross Sea‐ and Adelie Land‐sourced AABW are exported mainly to the Pacific Ocean. Northward transport of each tracer occurs almost exclusively (>90%) within a single conduit. These findings imply that regional changes in AABW production may impact the three‐dimensional structure of the global overturning circulation.