No description available.

No description available.

No description available.

No description available.

Students who graduate with an advanced degree in mechanical engineering are a diverse group in their path to post-baccalaureate degree attainment. Some students choose to obtain their master’s or Ph.D. post bachelors, but before they enter the workplace. Others enter the workforce and return as full-time students or progress on their advanced degrees while maintaining part- or full-time employment. Current accreditation standards for undergraduate degree programs are part of a changing landscape of standards and professional requirements that have adapted and continue to adapt as programs prepare students to work in professional engineering fields. Advanced degrees do not have the same set of standards as accredited undergraduate programs that are modified and examined for continuous improvement of the preparation of students for professional and academic careers. Without this overall agreement, what are advanced degree programs offering students and what skills should the programs be addressing the most? This research develops an understand of what the technical, professional, and academic requirements are expected for students seeking employment or continuing to advance in their chosen careers.

The Inflation Reduction Act has allocated over $369 billion to expedite the transition from fossil fuels to renewable energy. Along with these incentives, the funds support job training initiatives, like the recently introduced American Climate Corps. The transition to new energy forms will result in structural changes in the labor market and the demand for new and emerging skills, tasks, and jobs. A challenge, however, is that there are no existing definitions of what constitutes green jobs and skills, and thus, no clear consensus on the training workers will need for these jobs. This dissertation employs a data-driven approach using the Occupational Information Network to define and characterize green tasks, skills, and jobs. Using Natural Language Processing, we develop a method to quantify the "greenness'' of tasks and occupations. Utilizing this index, we explore the significant role of green skills during economic transitions. Our findings offer a comprehensive roadmap for understanding the evolution of green jobs and skills over the next decade. This dissertation comprises three chapters analyzing the tasks, skills, and jobs in the green economy. The first chapter investigates what constitutes green jobs and their characteristics. We construct "Task Greenness Scores" and "Occupational Green Potential" indices using Natural Language Processing and machine learning techniques to assess the greenness of tasks and overall occupations. Clustering methods categorize occupations based on task attributes -- green potential, frequency, importance, and relevance, identifying five distinct groups. This classification reveals significant variability in job greenness; although many jobs incorporate green tasks, only 113 occupations are definitively categorized as green. These are further divided into "High Green Intensity-Task Focus" and "High Green Intensity-Use Focus" groups, with the latter typically requiring less formal education and emphasizing manual skills over analytical or interactive skills. Our analysis also indicates a modest overall unconditional green wage premium of 3% for 2019 and 2020. The second chapter delineates green skills and maps their prevalence across the U.S., focusing on coal-mining communities in Appalachia. We sort a variety of skills into categories reflecting task and skill differences between green and non-green occupations, identified through O*NET. Principal Component Analysis helps categorize these into broader green skill groups such as "Technical Skills", "Management Skills", "Science Knowledge", and "Integrated Knowledge". The prevalence of green skills is notable in production-related occupations, suggesting essential technical expertise for the green economy. Interestingly, sectors traditionally viewed as energy-intensive also show a foundation conducive to green practices. Our findings highlight the necessity of tailored training programs that cater to diverse educational backgrounds, particularly emphasizing the lack of green skills in Appalachian regions, which may exacerbate inequalities during the economic transition. The third chapter examines the mediating role of green skills in local labor markets amidst the transition to a sustainable and energy-efficient economy. This chapter informs policy debates on large-scale green fiscal plans of the 2009 American Recovery and Reinvestment Act. We discover that regions well-prepared for environmental regulations or new energy development benefit from a robust stock of green skills. However, our analysis suggests that green ARRA investments are negatively correlated with wages and job creation, contrasting with positive correlations found in non-green ARRA investments. This chapter concludes that green skills significantly influence labor market outcomes, particularly in the manufacturing sector, and highlights the spillover effects of green stimulus on neighboring labor markets. / Doctor of Philosophy / This dissertation examines jobs, tasks, and skills in the green economy which promotes renewable energy and environmental sustainability. The transition to renewable energy requires new skills and tasks, but there is no clear definition of what constitutes green jobs and skills, nor an understanding of their distribution across occupations, industries, and geographic regions. This study uses a data-driven approach to construct an index that quantifies, defines, and characterizes green tasks, skills, and jobs. Overall, this dissertation provides a comprehensive analysis of green jobs and skills, offering insights into the evolving labor market and the necessary training programs to support this transition. The first chapter identifies what makes a job green and categorizes occupations based on their green potential. The analysis reveals significant variability in job greenness and shows that while many jobs include green tasks, only a small number are definitively green, with a modest wage premium for green jobs. The second chapter maps the distribution of green skills across the U.S., with a focus on coal-mining communities in Appalachia. It highlights the technical expertise required for green jobs and the need for tailored training programs to address skill gaps, particularly in regions like Appalachia. The third chapter explores the mediating role of green skills in labor market outcomes during the transition to a sustainable economy. It finds that regions with a strong stock of green skills fare better under environmental regulations and new energy development. Green investments from the 2009 American Recovery and Reinvestment Act show mixed effects on wages and job creation compared to non-green investments.

Search and Rescue (SAR) operations are necessary during times of natural disaster or when individuals go missing. These missions mobilize individuals, both paid and volunteer, to find lost persons and are often carried out in treacherous areas. It is important for teammates to be focused and prepared. Specifically, the search coordinator's role and the head of command directing their teammates is vital to the outcome of a SAR mission. Their workload is significant, however, inviting the opportunity for autonomy to work in tandem with the search coordinator to ensure optimal, timely decisions are made for task scheduling. There has been a significant amount of investigation into task scheduling for human-autonomy teams, but there is a gap in the ordering methods used. One possible framework to investigate uses Rasmussen's SRK framework to classify individual responses to assigned tasks. There is also a significant body of work on this framework, but very little in a proactive task scheduling context. This thesis proposes a new approach to task scheduling utilizing Rasmussen's SRK framework. Scheduling tasks in this manner allows the characteristics of the tasks themselves to be considered as well as a more streamlined approach to reducing overall cognitive load for SAR teammates. An experimental study was conducted to determine the effectiveness of the proposed task scheduling methods based on the SRK framework. Initial results suggest there is an impact on scheduling tasks with respect to SRK, but further investigation is warranted to determine more specific factors. / Master of Science / Search and Rescue (SAR) operations are necessary during times of natural disaster or when individuals go missing. These missions mobilize individuals, both paid and volunteer, to find lost persons and are often carried out in treacherous areas. It is important for teammates to be focused and prepared. Specifically, the search coordinator's role and the head of command directing their teammates is vital to the outcome of a SAR mission. They have a significant number of responsibilities, however, inviting the potential for becoming overloaded with information and decisions. This does, however, suggest a significant opportunity for autonomy to work in tandem with the search coordinator to ensure optimal, timely decisions are made for task scheduling. There has been a significant amount of investigation into task scheduling for human-autonomy teams, but there is a gap in the ordering methods used. One possible framework to use to fill this gap is Rasmussen's Skill-Rule-Knowledge (SRK) framework to categorize and predict how individuals will respond to their assigned tasks. There is also a significant body of work on this framework, but very little in a proactive task scheduling context. This thesis proposes a new approach to task scheduling utilizing Rasmussen's SRK framework. Scheduling tasks in this manner allows the characteristics of the tasks themselves to be considered. In addition, the possibility of overwhelming SAR team members can be reduced by scheduling tasks intelligently. An experimental study was conducted to determine the effectiveness of the proposed task scheduling methods based on the SRK framework. Initial results suggest there is an impact on scheduling tasks with respect to SRK, but further investigation is warranted to determine more specific factors.

The problem of this research was to determine whether using hand-held calculators was more effective for the acquisition and retention of concepts and skills on decimal algorithms and metric units than the use of pencil and paper computation only. The sample consisted of six intact sixth grade classes (175 students). Two classes hand-held calculators (the experimental group) and one class using pencil and paper only (the control group) were located in each of two schools in separate school districts. The classes were assigned randomly as either experimental or control. Each treatment period was 30-50 minutes daily for the duration of the twenty-five day study. Both groups studied the same content based on designated learning objectives. Test scores of the Criterion Referenced Test in Metrics Measurement by Heber and a decimal test, developed by the researcher, were used as dependent variables. Both tests were used as pretests, posttests, and retention tests. The multivariate analysis of covariance technique was used to test the hypotheses. / Ed. D.

Differences were investigated between two methods of teaching English in a community college setting-~one method emphasizing reading/writing skills and one emphasizing writing skills. A pre-test/post~test design was used for the study. The criterion evaluated were reading skills (vocabulary and comprehension) and writing skills. The multivariate analysis of covariance (MANCOVA) was the Statistical treatment applied to the data. The null hypothesis that there were no differences between the two methods of teaching English was not rejected. There were significant differences, however, among the three participating teachers and group/teacher interaction. While there were those who indicated the English class as the appropriate place to integrate the skills of reading and writing, the study did not support this idea. However, it was recommended that the study be made in other community colleges with modification of the design to include a longer period of time between pre- and post-testing. / Ed. D.

The problem of this study was to ascertain to what degree a bias recognition program would act to increase the bias perception level of eighth grade social studies students. Hypotheses were set covering five bias recognition subtest areas: ethnocentric, religious, political, sexist and racial bias recognition. Two areas, racial and sexist bias recognition, were to undergo two-way analysis. Sexist bias recognition was analyzed for differences by control and experimental group, sex, and by sex and group. The racial bias recognition subtest area was also analyzed by experimental and control group, by race, and by race and group. A review of the available literature revealed little in the way of work previously done concerning bias recognition. Some efforts had been made in the area of communication skills. The review itself was divided into three areas: 1. articles and studies dealing with communication skills 2. testing and research studies in skills 3. articles dealing with bias recognition as a skill area The review revealed only one prior research study had been done in the area of skills. This study was accomplished by Dan B. Fleming and Larry Weber and concerned a wide range of skills. Two excerpts from articles were found that concerned bias recognition as a skill. However, no research studies were found in this area. After available literature was reviewed, a bias recognition test was designed to test the aforementioned five bias recognition subtest areas as well as the total test area. This test was administered in the three experimental classes located in three different junior high schools in Roanoke, Virginia. The test was also administered to three classes located in the three remaining junior high schools. In this way each junior high school contained one treatment or control class. After an approximate thirteen school days period, the classes were retested. During this period between tests the experimental classes received instruction in the form of a bias recognition program. Data from these tests was analyzed using both descriptive and inferential statistical techniques. The statistical application of most importance was multivariate and univariate analysis of covariance. In covariance analysis of a pretest - posttest design, posttest scores are adjusted for pretest scores in order to statistically control for any initial differences that may have existed between the groups at the time of the pretest. Descriptive analysis showed the experimental group exhibited superior achievement on the posttest by total and subtest areas. Inferential analysis further showed a significant difference existed between groups for the total score as well as all the five subtest areas of ethnocentric, religious, political, sexist and racial bias recognition. The two subtest areas exhibiting the greatest success were political and sexist bias recognition. No significant difference was found to exist in the sexist bias recognition subtest area by sex or sex and group. In the racial bias recognition subtest area significant differences were not shown to exist by race, but did exist by race and group. Simultaneous confidence intervals analysis further showed that the most effective combination was black students that had received the treatment, compared to black students not receiving the treatment. Conclusions from this study showed positive results in all subtest and total areas pointing toward a need for a more systematic approach toward skills-oriented curriculum in public schools. One facet of the study of importance is a development of a bias recognition test that can be further refined toward the goa1 of more effective measurement of this skills area. / Ed. D.