scholarly journals Going for the Gold

10.28945/4558 ◽  
2019 ◽  
Vol 4 ◽  
pp. 001-020
Author(s):  
Eric Johnson ◽  
Lanel Menezes ◽  
Tim Routier ◽  
Mikaela Walter ◽  
Keith White
Keyword(s):  
Community College ◽  
Performance Metrics ◽  
Performance Funding ◽  
Performance Model ◽  
Incentive Program ◽  
Funding Model ◽  
College System ◽  
Gold Silver ◽  
The Right ◽  
Faculty And Administrators

Dr. Ken Atwater, President of Hillsborough Community College (HCC), saw the email flash across his monitor, “2018-19 Performance Funding: Hillsborough Community College” sent from the Chancellor of the Florida College System (FCS). The email was 12 months in the making. In 2015, the Florida Legislature created the first performance funding-based incentive program in its General Appropriations Act (Laws of Florida Ch. 2015-232. (n.d.)). Proviso language required the State Board of Education to allocate performance funds pursuant to a performance funding model. The performance model had four performance funding metrics: retention, graduation, wages and job placement (see Exhibit 1). This one message would answer a burning question that had been lingering in the college’s top administrators’ minds: Where would the college land in another year of performance funding? Atwater contemplatively read the email, “A Bronze ranking, again.” This Bronze designation meant the college was not eligible for new state distributed performance funds meaning almost $2 million would not be appropriated to HCC. Atwater asked himself “what needs to be done so HCC is eligible for this funding?” The 2015 Florida Legislature inserted language into its General Appropriations Act creating the FCS’ performance funding-based incentive program. The direction of millions of dollars distributed throughout Florida colleges had been determined including a final ranking of Gold, Silver, Bronze or Purple for each college, with Gold being the highest ranking. This ranking determined whether HCC received millions in new dollars; money that in an environment of budget cuts to the entire FCS over the last two years would be extremely important to the students, faculty, and administrators across Hillsborough County. Atwater knew the college needed to improve its score, thus allowing HCC to move into a Silver or Gold category. The improvement in the score to gain the additional dollars boiled down to concentrated efforts in providing the best education for students while equipping faculty with the right resources to improve effectiveness. Atwater thought, "Easier said than done. I am faced with the proverbial chicken before the egg or egg before the chicken. I may need funding to make the necessary changes to improve the scores. However, without the necessary changes to show improvement in scores, we will not receive the funding." Regardless of the dilemma, the question had to be asked, "What strategies should be implemented to increase scores in the four performance metrics that the college would be judged on? Should the college expand tracking of the cohort of students that is examined? Should new student success initiatives be rolled out to help students?" Atwater wanted answers. He had approximately two million reasons why.

THE EVOLUTION OF THE COMMUNITY COLLEGE SYSTEM IN NEBRASKA

1997 ◽  
Vol 21 (6) ◽  
pp. 543-558
Author(s):  
Robert P. Cox ◽  
Robert J. Waddell ◽  
Sharon A. Howell ◽  
Anne F. Ausdemore
Keyword(s):  
Community College ◽  
Community College System ◽  
College System

Effect of Manufacturing Variability on Turbine Engine Performance: A Probabilistic Study

2013 ◽  
Author(s):  
Michael Gorelik ◽  
Jacob Obayomi ◽  
Jack Slovisky ◽  
Dan Frias ◽  
Howie Swanson ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Model Development ◽  
Engine Performance ◽  
Performance Model ◽  
Probabilistic Methods ◽  
System Level ◽  
Turbine Engine ◽  
Probabilistic Study ◽  
Blade Geometry

While turbine engine Original Equipment Manufacturers (OEMs) accumulated significant experience in the application of probabilistic methods (PM) and uncertainty quantification (UQ) methods to specific technical disciplines and engine components, experience with system-level PM applications has been limited. To demonstrate the feasibility and benefits of an integrated PM-based system, a numerical case study has been developed around the Honeywell turbine engine application. The case study uses experimental observations of engine performance such as horsepower and fuel flow from a population of engines. Due to manufacturing variability, there are unit-to-unit and supplier-to-supplier variations in compressor blade geometry. Blade inspection data are available for the characterization of these geometric variations, and CFD analysis can be linked to the engine performance model, so that the effect of blade geometry variation on system-level performance characteristics can be quantified. Other elements of the case study included the use of engine performance and blade geometry data to perform Bayesian updating of the model inputs, such as efficiency adders and turbine tip clearances. A probabilistic engine performance model was developed, system-level sensitivity analysis performed, and the predicted distribution of engine performance metrics was calibrated against the observed distributions. This paper describes the model development approach and key simulation results. The benefits of using PM and UQ methods in the system-level framework are discussed. This case study was developed under Defense Advanced Research Projects Agency (DARPA) funding which is gratefully acknowledged.

The Necessity of Performance Measures for Prosecutors

2021 ◽  
pp. 226-258
Author(s):  
Lauren-Brooke Eisen ◽  
Miriam Aroni Krinsky
Keyword(s):  
Performance Measures ◽  
Performance Metrics ◽  
Mass Incarceration ◽  
The United States ◽  
New Wave ◽  
Criminal Cases ◽  
Critical Data ◽  
District Attorneys ◽  
The Right ◽  
Individual Line

Local prosecutors are responsible for 95 percent of criminal cases in the United States—their charging decisions holding enormous influence over the number of people incarcerated and the length of sentences served. Performance metrics are a tool that can align the vision of elected prosecutors with the tangible actions of their offices’ line attorneys. The right metrics can provide clarity to individual line attorneys around the mission of the office and the goals of their job. Historically, however, prosecutor offices have relied on evaluation metrics that incentivize individual attorneys to prioritize more punitive responses and volume-driven activity—such as tracking the number of cases processed, indictments, guilty pleas, convictions, and sentence lengths. Under these past approaches, funding, budgeting, and promotional decisions are frequently linked to regressive measures that fail to account for just results. As more Americans have embraced the need to end mass incarceration, a new wave of reform-minded district attorneys have won elections. To ensure they are accountable to the voters who elected them into office and achieve the changes they championed, they must align measures of success with new priorities for their offices. New performance metrics predicated on the goals of reducing incarceration and enhancing fairness can shrink prison and jail populations, while improving public trust and promoting healthier and safer communities. The authors propose a new set of metrics for elected prosecutors to consider in designing performance evaluations, both for their offices and for individual attorneys. The authors also suggest that for these new performance measures to effectively drive decarceration practices, they must be coupled with careful, thoughtful implementation and critical data-management infrastructure.

Estimation of Design Parameters and Performance for a State-of-the-Art Turbofan

2021 ◽  
Author(s):  
Oliver Sjögren ◽  
Carlos Xisto ◽  
Tomas Grönstedt
Keyword(s):  
Performance Metrics ◽  
State Of The Art ◽  
Low Pressure ◽  
Performance Model ◽  
Cycle Performance ◽  
Civil Aviation ◽  
Design Parameters ◽  
Pressure System ◽  
Public Data ◽  
Low Pressure System

Abstract The aim of this study is to explore the possibility of matching a cycle performance model to public data on a state-of-the-art commercial aircraft engine (GEnx-1B). The study is focused on obtaining valuable information on figure of merits for the technology level of the low-pressure system and associated uncertainties. It is therefore directed more specifically towards the fan and low-pressure turbine efficiencies, the Mach number at the fan-face, the distribution of power between the core and the bypass stream as well as the fan pressure ratio. Available cycle performance data have been extracted from the engine emission databank provided by the International Civil Aviation Organization (ICAO), type certificate datasheets from the European Union Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA), as well as publicly available data from engine manufacturer. Uncertainties in the available source data are estimated and randomly sampled to generate inputs for a model matching procedure. The results show that fuel performance can be estimated with some degree of confidence. However, the study also indicates that a high degree of uncertainty is expected in the prediction of key low-pressure system performance metrics, when relying solely on publicly available data. This outcome highlights the importance of statistic-based methods as a support tool for the inverse design procedures. It also provides a better understanding on the limitations of conventional thermodynamic matching procedures, and the need to complement with methods that take into account conceptual design, cost and fuel burn.

Equity or Disparity: Do Performance Funding Policies Disadvantage 2-Year Minority-Serving Institutions?

2018 ◽  
Vol 46 (3) ◽  
pp. 288-315 ◽  
Author(s):  
Amy Y. Li ◽  
Denisa Gándara ◽  
Amanda Assalone
Keyword(s):  
Developmental Education ◽  
Performance Metrics ◽  
Underrepresented Students ◽  
Performance Funding ◽  
State Funding ◽  
Retention Rates ◽  
Gateway Courses ◽  
Funding Policies ◽  
Funding Formulas ◽  
Minority Serving Institutions

Objective: We investigate whether performance funding—an increasingly prevalent state policy that allocates appropriations based on outcomes that prioritize retention and completion—places minority-serving institutions (MSIs) at a financial disadvantage due to these institutions serving a greater proportion of historically underrepresented students. Method: Using data from 2004-05 to 2014-15 within Texas and Washington, we compare state funding allocations to 2-year institutions designated as MSIs versus non-MSIs, before and after performance funding policies are implemented. We additionally compare funding allocations for each performance metric. Results: On average, MSIs in Texas and Washington are allocated the same or less in per-student state funding after performance funding compared to non-MSIs. MSIs in Texas are advantaged in performance metrics for transfers and for gateway courses in math (credit-bearing courses that serve as a “gateway” to continued study), and MSIs in Washington are advantaged in developmental education courses. However, MSIs are typically disadvantaged in metrics for degree completions. Conclusion: Our findings suggest that MSIs in Texas and Washington are not financially disadvantaged due to performance funding because the funding formulas in both states incentivize milestones in addition to outputs. We recommend that policy makers consider incorporating performance metrics for developmental education and gateway courses in addition to retention rates and degree completions, and tailor metrics to the student population of institutions to mitigate the potentially inequitable funding consequences of performance funding policies.

Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers

2021 ◽  
Vol 25 (5) ◽  
pp. 1073-1098
Author(s):  
Nor Hamizah Miswan ◽  
Chee Seng Chan ◽  
Chong Guan Ng
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Performance Metrics ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Set Up ◽  
The Right ◽  
The Impact

Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.

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