Will I Get In? Using Predictive Analytics to Develop Student-Facing Tools to Estimate University Admissions Decisions

A sizable number of low-income high school graduates enroll in colleges less selective than their academic qualifications would allow or forgo postsecondary altogether despite being college-ready. One potential cause of this “undermatching” is that some students have limited access to information about their college options. We hypothesize that providing students with more and better information about the relationship between their academic preparation and college options may promote college-going. The purpose of this study was to develop a predictive model of admissions to public 4-year institutions using data from Texas’ statewide longitudinal data system in order to build a student-facing tool that predicts admissions decisions. We sought to include only variables for which students have some control over, namely academic characteristics, but compared the predictive accuracy of this reduced model to more complex models that include demographic variables commonly used in higher education research. We show the reduced model successfully predicts admissions decisions for approximately 85% of applications. The addition of demographic variables, despite showing a statistically significant better fit of the data, do not substantively change the predictive accuracy of the model. We include a demonstration of a data visualization tool built on this predictive model using the open-source R statistical software that can be used by students, parents, and educators. We also discuss causes for both optimism and caution when using predictive modeling to develop student-facing tools

Update on the Fishes of Texas Project

Poster presentation presented at the 2017 Texas Academy of Sciences annual meeting in Belton, Texas on March 4, 2017.The Fishes of Texas project (www.fishesoftexas.org), originating in 2006, remains the most reliable (quality controlled) and data rich site for acquiring occurrence data for Texas fishes, holding over 124,000 records from 42 institutions. Among many discoveries, the project is responsible for detecting at least 3 freshwater species not previously known from the state. We continue making improvements, but substantial updates so far have been onerous for our developers for various reasons. A recent major update reduces coding redundancies, points the website to a new massively restructured and more fully normalized PostgreSQL database (was MySQL), and places the code in a versioning environment. These changes have little immediate effect on user experience, but will greatly accelerate development. PostgreSQL allows for complex spatial queries which will allow users to quickly map occurrence data alongside many more political/environmental layers than currently possible. While our database/web designers have been implementing these changes and fixing bugs etc., we’ve been preparing resources for them to integrate into the website. Some highlights to expect: 1 new updates to the state Species of Greatest Concern list; 2 expert opinion-determined nativity spatial layers for all freshwater fishes displaying in our new mapping system; 3 dynamic statistical summaries; 4 new data types from the literature (>14,900 records), citizen science (>4,300), anglers (>37,000), and agency databases (>1,000,000); 5 new museum records, many derived from our gap sampling (~19,000, 4 museums); 6 more specimen examinations (>400) and photographs (1000); 7 document archive with “smart” text search tools (currently in beta testing using TPWD fisheries reports). So be patient and keep your eyes open for updates.University of Texas at Austin, Texas Parks and Wildlife Department, Texas Commission on Environmental Quality, U.S. Department of the Interior,Integrative Biolog

Automating the Extraction of Metadata from Archaeological Data Using iRods Rules

The Texas Advanced Computing Center and the Institute for Classical Archaeology at the University of Texas at Austin developed a method that uses iRods rules and a Jython script to automate the extraction of metadata from digital archaeological data. The first step was to create a record-keeping system to classify the data. The record-keeping system employs file and directory hierarchy naming conventions designed specifically to maintain the relationship between the data objects and map the archaeological documentation process. The metadata implicit in the record-keeping system is automatically extracted upon ingest, combined with additional sources of metadata, and stored alongside the data in the iRods preservation environment. This method enables a more organized workflow for the researchers, helps them archive their data close to the moment of data creation, and avoids error prone manual metadata input. We describe the types of metadata extracted and provide technical details of the extraction process and storage of the data and metadata

Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models

Progress in hybrid physics-machine learning (ML) climate simulations has been limited by the difficulty of obtaining performant coupled (i.e. online) simulations. While evaluating hundreds of ML parameterizations of subgrid closures (here of convection and radiation) offline is straightforward, online evaluation at the same scale is technically challenging. Our software automation achieves an order-of-magnitude larger sampling of online modeling errors than has previously been examined. Using this, we evaluate the hybrid climate model performance and define strategies to improve it. We show that model online performance improves when incorporating memory, a relative humidity input feature transformation, and additional input variables. We also reveal substantial variation in online error and inconsistencies between offline vs. online error statistics. The implication is that hundreds of candidate ML models should be evaluated online to detect the effects of parameterization design choices. This is considerably more sampling than tends to be reported in the current literature.Comment: 13 pages, 4 figure

Focus, Vol. 1 No. 1

A literary magazine of student writing published by the Department of English of Stephen F. Austin State College.https://scholarworks.sfasu.edu/focus/1000/thumbnail.jp

Teaching for the transition: The Canadian PGY-1 neurosurgery \u27rookie camp\u27

Background: Transitioning from medical school to residency is difficult and stressful, necessitating innovation in easing this transition. In response, a Canadian neurosurgical Rookie Camp was designed and implemented to foster acquisition of technical, cognitive and behavioral skills among incoming Canadian post graduate year one (PGY-1) neurosurgery residents. Methods: The inaugural Rookie Camp was held in July 2012 in Halifax. The curriculum was developed based on a national needs-assessment and consisted of a pre-course manual, 7 case-based stations, 4 procedural skills stations and 2 group discussions. The content was clinically focused, used a variety of teaching methods, and addressed multiple CanMEDS competencies. Evaluation included participant and faculty surveys and a pre-course, post-course, and 3-month retention knowledge test. Results: 17 of 23 PGY-1 Canadian neurosurgical residents participated in the Camp. All agreed the course content was relevant for PGY-1 training and the experience prepared them for residency. All participants would recommend the course to future neurosurgical residents. A statistically significant improvement was observed in knowledge related to course content (F(2,32) = 7.572, p\u3c0.002). There were no significant differences between post-test and retention-test scores at three months. Conclusion: The inaugural Canadian Neurosurgery Rookie Camp for PGY-1 residents was successfully delivered, with engagement from participants, training programs, the Canadian Neurosurgical Society, and the Royal College. In addition to providing fundamental knowledge, which was shown to be retained, the course eased junior residents\u27 transition to residency by fostering camaraderie and socialization within the specialty

Goals of Care Documentation: Insights from A Pilot Implementation Study

ContextThe Life Sustaining Treatment Decision Initiative is a national effort by the Veterans Health Administration to ensure goals of care documentation occurs among all patients at high risk of life-threatening events. ObjectivesExamine likelihood to receive goals of care documentation and explore associations between documentation and perceived patient care experience at the individual and site level. MethodsRetrospective, quality improvement analysis of initiative pilot data from four geographically diverse Veterans Affairs (VA) sites (Fall 2014-Winter 2016) before national roll-out. Goals of care documentation according to gender, marital status, urban/rural status, race/ethnicity, age, serious health condition, and Care Assessment Needs scores. Association between goals of care documentation and perceived patient care experience analyzed based on Bereaved Family Survey outcomes of overall care, communication, and support. ResultsVeterans were more likely to have goals of care documentation if widowed, urban residents, and of white race. Patients older than 65-years and those with a higher Care Assessment Needs score were twice as likely as a frail patient to have goals of care documented. One pilot site demonstrated a positive association between documentation and perceived support. Pilot site was a statistically significant predictor of the occurrence of goals of care documentation and Bereaved Family Survey scores. ConclusionOlder and seriously ill patients were most likely to have goals of care documented. Association between a documented goals of care conversation and perceived patient care experience were largely unsupported. Site-level largely contributed to understanding the likelihood of documentation and care experience

Development and Feasibility of a Structured Goals of Care Communication Guide

BackgroundDiscussing goals of care and advance care planning is beneficial, yet how to best integrate goals of care communication into clinical care remains unclear.ObjectiveTo develop and determine the feasibility of a structured goals of care communication guide for nurses and social workers.Design/setting/subjectsDevelopmental study with providers in an academic and Veterans Affairs (VA) health system (n = 42) and subsequent pilot testing with patients with chronic obstructive pulmonary disease or heart failure (n = 15) and informal caregivers (n = 4) in a VA health system. During pilot testing, the communication guide was administered, followed by semistructured, open-ended questions about the content and process of communication. Changes to the guide were made iteratively, and subsequent piloting occurred until no additional changes emerged.MeasurementsProvider and patient feedback to the communication guide.ResultsIterative input resulted in the goals of care communication guide. The guide included questions to elicit patient understanding of and attitudes toward the future of illness, clarify values and goals, identify end-of-life preferences, and agree on a follow-up plan. Revisions to guide content and phrasing continued during development and pilot testing. In pilot testing, patients validated the importance of the topic; none said the goals of care discussion should not be conducted. Patients and informal caregivers liked the final guide length (∼30 minutes), felt it flowed well, and was clear.ConclusionsIn this developmental and pilot study, a structured goals of care communication guide was iteratively designed, implemented by nurses and social workers, and was feasible based on administration time and acceptability by patients and providers