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HI line observations of nearby galaxies often reveal the presence of extraplanar and/or kinematically anomalous gas that deviates from the general circular flow. In this work, we study the dependence of kinematically anomalous HI gas in galaxies taken from the SIMBA cosmological simulation on galaxy properties such as HI mass fraction, specific star formation rate, and local environmental density. To identify kinematically anomalous gas, we use a simple yet effective decomposition method to separate it from regularly rotating gas in the galactic disc; this method is well-suited for application to observational data sets but has been validated here using the simulation. We find that at fixed atomic gas mass fraction, the anomalous gas fraction increases with the specific star formation rate. We also find that the anomalous gas fraction does not have a significant dependence on a galaxy’s environment. Our decomposition method has the potential to yield useful insights from future HI surveys. 

Integral field spectroscopy of high-redshift galaxies has become a powerful tool for understanding their dynamics and evolutionary states. However, in the case of gravitationally lensed systems, it has proved difficult to model both lensing and intrinsic kinematics in a way that takes full advantage of the information available in the spectral domain. In this paper, we introduce a new method for pixel-based source reconstruction that alters standard regularization schemes for two-dimensional (2D) data in a way that leverages kinematic information in a physically motivated but flexible fashion, and that is better suited to the three-dimensional (3D) nature of integral field data. To evaluate the performance of this method, we compare its results to those of a more traditional 2D nonparametric approach using mock Atacama Large Millimeter/submillimeter Array (ALMA) observations of a typical high-redshift dusty star-forming galaxy. We find that 3D regularization applied to an entire data cube reconstructs a source’s intensity and velocity structure more accurately than 2D regularization applied to separate velocity channels. Cubes reconstructed with 3D regularization also have more uniform noise and resolution properties and are less sensitive to the signal-to-noise ratio of individual velocity channels than the results of 2D regularization. Our new approach to modeling integral field observations of lensed systems can be implemented without making restrictive a priori assumptions about intrinsic kinematics, and opens the door to new observing strategies that prioritize spectral resolution over spatial resolution (e.g., for multiconfiguration arrays like ALMA).

 

Galaxy evolution is regulated by the continuous cycle of gas accretion, consumption and feedback. Crucial in this cycle is the availability of neutral atomic (HI) and molecular hydrogen. Our current inventory of HI, however, is very limited beyond the local Universe (z > 0.25), resulting in an incomplete picture. ORCHIDSS is designed to address this critical challenge, using the powerful combination of 4MOST spectroscopy and sensitive radio observations from the MeerKAT deep extragalactic surveys to trace the evolution of neutral gas and its lifecycle within galaxies across the bulk of cosmic history. 

Abstract — In this Full Research Paper, we propose a new definition of overpersistence in an engineering discipline and investigate its implications at one institution. Precisely defining overpersistence in both a conceptual and operational sense is a critical step in predicting overpersistence and identifying indicators that will allow for personalized guidance for students at risk of overpersisting. We have previously identified our population of interest as students who enroll at the institution as first-time-in-college students for at least one year, attend full time, have had six years to graduate, and have enrolled in only one degree-granting program. Within this group, we operationalized overpersistence by identifying students as overpersisters if they either (i) left the university without a degree or (ii) enrolled in the same major for six years and did not graduate. In this work, we revisit our definition of overpersistence using more recent data by reconsidering two groups of students in particular – those who spend only a short time in the discipline before leaving the institution (formerly classified as overpersisters), and those who spend a long time in the discipline but eventually switch majors (formerly excluded from the initial population). We conclude that the most appropriate definition of overpersistence at an institution with a first-year engineering program is when a student spends three or more semesters in their first discipline-specific major and does not graduate in that major within six years of matriculation to the institution. These results will be useful for researchers and practitioners seeking to identify alternative paths for success for students who are at risk of overpersisting in a major. 

The main objective of this project is to help students learn to make decisions that lead to academic success. Our first goal is to map curriculum pathways, which begins by studying overpersistence (when a student persists in a particular major but does not make timely progress toward a degree). We seek to identify curriculum-specific indicators of overpersistence and corresponding alternative paths that could lead to success. Our second goal is to improve the structure of the Decision-Making Competency Inventory (DMCI) so that it can explain student's decision-making competency in more detail and in congruence with the Self-Regulation Model of Decision-Making. This instrument will be used to map decision-making competency to academic choices and outcomes. The third goal is to develop an Academic Dashboard as a means for sharing relevant research results with students. This will allow students to have access to the strategies, information, and stories needed to make and implement adaptive decisions. This paper highlights our progress in the fifth year of the project and our plans going forward. 

This paper provides a summary activities and accomplishments of an NSF CAREER project, “Empowering Students to be Adaptive Decision-Makers.” We discuss our progress on (1) identifying indicators of poor academic fit in engineering majors; (2) examining relationships between the measures of theoretical constructs (Decision-Making Competency Inventory, DMCI) with the real-world, academic behaviors (major choice and major change); (3) revisions to the DMCI; and (4) development of the Academic Dashboard for putting students in the driver’s seat of their education. A prototype of the Academic Dashboard and its functionality are described. 

The objective of this EEC project is to help students learn to make academic decisions that lead to success. The research goals are to: 1) identify curriculum-specific patterns of achievement that eventually lead to dropout and corresponding alternative paths that could lead to success; and 2) advance knowledge of self-regulation patterns and outcomes in engineering students. The education goals are to develop curricula and advising materials that help students learn how to effectively self-regulate their decision processes through contextual activities and story prompting. This poster will present current progress and future directions of the project. We will summarize accomplishments on the development of the Self-Regulated Decision-Making instrument, mapping of pathways, and development of the academic dashboard. 

This complete research paper documents how confidence in choice of intended major and self-regulated decision-making competency influence whether a student changes their intended major while participating in a compulsory first-year engineering (FYE) program. Initial major, confidence in that major choice, and self-regulated decision-making competency were documented in the Fall of 2017 for students matriculating into a FYE program. Student enrollment in a major in the Fall of 2018 was connected to this data. Retention in any engineering major and in the student’s intended major were analyzed using logistic regression. 

This Full Research Paper discusses ongoing work to develop a survey instrument to reliably assess undergraduate engineering student self-regulated decision-making. This work focuses on a second round of item expansion and refinement to the Decision-Making Competency Inventory (DMCI) to develop items related to learning from past decisions. The refined instrument was distributed to first-year engineering students enrolled in a large, public, land-grant institution located in the southeastern United States in the Fall of 2018. Of the approximately 1,200 students in first-year engineering courses, 883 valid surveys were randomly split into two separate samples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA results indicated a viable four-factor solution, which was explored with the CFA. The CFA results also indicated a four-factor model was appropriate. Improving this instrument will help researchers document and understand students’ decision-making skills and how they relate to observed decisions like initial choice of major or change of major. A decision-making instrument will also be valuable in evaluating the effectiveness of interventions to help students build their decision-making competency and make adaptive choices.