Tom L. Schenk

Developing a Conceptual and Operational Definition of Stem for Iowa Community Colleges

Numerous papers have identified weakening of math and science education in the United States. In addition, numerous researchers and policy makers have emphasized STEM (science, technology, engineering, and mathematics) programs in secondary and postsecondary schools. After conducting review of surrounding Midwestern state governments, national institutions, and nonprofits, we find an inconsistent definition of STEM is being employed. We propose a conceptual definition of STEM based on the history and philosophy of science. We operationalize the definition by choosing a list of programs that fit within the framework. Finally, we use the definition to evaluate whether women and minorities are equally represented in STEM programs at Iowa community colleges. While minorities are proportionally represented, females are significantly underrepresented.

Calculating Returns to Degree Using Student Longitudinal Data Systems

Human capital theory shows educated workers are more productive in the workforce and subsequently earn more. Hundreds of studies have shown this by estimating the rate of return to education. However, these studies use national surveys and are limited to nationwide estimates. The emergence of Student Longitudinal Data System (SLDS) has permitted states and institutions to track students throughout education and even into the workforce. While several studies use SLDS data to calculate average wages of college alumni, none have calculated the rate of return. This paper develops a framework to calculate the net present value and rate of return for higher education using SLDS data. We apply the framework to estimate the economic value of completing a degree at an Iowa community college. We estimate the returns for all community colleges, by award type, and individual programs. Our results show returns are six percent for those completing a community college degree.

The Effects of Graduate-Student Unionization on Stipends

Beginning in the 1970s, graduate assistants have organized labor unions. Presently, 38 universities have a graduate-student union. However, the effect graduate-student unions have on wages, wage variance, health benefits, and organizational structure is unknown. This study uses data from the Chronicle of Higher Education and government data to estimate the economic effects of unionization. By using a multilevel model is used to control for intra-university correlation of wages, this study concludes graduate unions are effective at raising stipends, but ineffective at lowering fees, providing health-care coverage, and lowering intra-university wage variance.

Machine-learned epidemiology: real-time detection of foodborne illness at scale

Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.

Predicting E. coli concentrations using limited qPCR deployments at Chicago beaches

Culture-based methods to measure Escherichia coli (E. coli) are used by beach administrators to inform whether bacteria levels represent an elevated risk to swimmers. Since results take up to 12 hours, statistical models are used to forecast bacteria levels in lieu of test results; however they underestimate days with elevated fecal indicator bacteria levels. Quantitative polymerase chain reaction (qPCR) tests return results within 3 hours but are 2 to 5 times more expensive than culture-based methods. This paper presents a prediction model which uses limited deployments of qPCR tested sites with inter-beach correlation to predict when bacteria will exceed acceptable thresholds. The model can be used to inform management decisions on when to warn residents or close beaches due to exposure to the bacteria. Using data from Chicago collected between 2006 and 2016, the model proposed in this paper increased sensitivity from 3.4 percent to 11.2 percent–a 230 percent increase. We find that the correlation between beaches are substantial enough to provide higher levels of precision and sensitivity to predictive models. Thus, limited deployments of qPCR testing can be used to deliver better predictions for beach administrators at lower cost and less complexity.

Measuring Transitions into the Workforce as a Form of Accountability

This paper explores the relationship between student major and industry of employment and its application to higher education accountability. Data provided by statewide longitudinal data systems (SLDS) has enabled state educational agencies and colleges to follow students into the workforce. While most studies have focused on wage outcomes, this shows how to use SLDS data to understand the correlation between major and industry. The transition into the workforce is an important outcome since it is an assessment of a college’s ability to develop specific, targeted sectors of the economy. We use SLDS data from Iowa to follow community college alumni from 2002 through 2008.