Abstract The primary purpose of this study was to determine if applicants who had an associate degree in the health sciences prior to acceptance to a physician assistant program would do better than those applicants without an associate degree in the health sciences on three measures of success of physician assistant education. The three measures of success used were graduation rates, scores on the Physician Assistant Knowledge Rating and Assessment Tool (PACKRAT), and performance on the national certifying exam, the Physician Assistant National Certification Examination (PANCE). Data used for this dissertation were taken from original source documents and raw data sent to Nova Southeastern University by the PACKRAT and PANCE testing services. The study population was the three classes graduating in 2007 to 2009. Correlations between the groups and their measures of success showed that there were no statistically significant difference in the graduation rates or PACKRAT scores (p-value was 0.328 and 0.095 respectively). The variable having a statistical significance was PANCE scores. The mean scores between the groups were significantly different (p-value 0.012) with the group without an associate degree in the health sciences having higher mean scores. Coincidental findings showed that older students and students with higher graduate records examination (GRE) scores did better on the PANCE. Following this, further data analysis showed that the group with an associate degree in the health sciences were older (p-value 0.06) and scored statistically lower on the GRE (p-value 0.012). Findings showed that many of the considerations used to select students for physician assistant programs did not make a difference in outcomes. The two that did were age and GRE scores. The study group with associate degrees in the health sciences was, on average, older, had lower mean GRE scores and demonstrated the most gender and ethnic diversity. Programs using admission data to select students for the best chance of success should consider student educational experience and GRE scores, especially when some schools are looking to increase diversity in the students entering their programs.

In order to meet the educational and economic demands of the United States in the future, institutions of higher education must increase the number of students who persist to the completion of a certificate or degree program, especially low-income students and students of color (Carnivale and Desrochers, 2004). To increase the persistence and completion rates of these students at community colleges, national initiatives, such as the Achieving the Dream: Community Colleges Count initiative, have emphasized the importance of creating institutional environments in which planning and improvement efforts are data-driven (Achieving the Dream, 2005). This study explored the perceptions of faculty members, administrators, and staff directly involved in establishing this data-driven environment, also known as a “culture of evidence,” and the extent to which those perceptions had disseminated through the larger college community. Through the use of a case study and focus groups using Interactive Qualitative Analysis (IQA) methods, the development of a “culture of evidence” at two Texas community colleges was examined as perceived by college constituents involved in its creation and by a group of college constituents indirectly influenced by their efforts. The emerging themes are discussed in their relation to promoting and maintaining a data- driven culture in the future. / text

Dual credit, which allows students to simultaneously earn high school and college credit for the same course, is widely-implemented across the country. Dual credit is thought to promote student success in higher education. However, there is limited research on whether dual credit courses taken in high school positively influence college-level outcomes. Using Ordinary Least Squares and Logistic analysis to control for student background characteristics, this study examines the relationship between dual credit and student success in college, specifically freshman grade point average and college graduation. The study examines an existing dataset from the Texas Higher Education Coordinating Board that includes approximately 35,870 students. Results suggest that dual credit positively influences college outcomes. / text

The purpose of this study was to explore individual perceptions of African-American at-risk students in an undergraduate teacher education program, specifically continuing students or community college transfer students in a four-year urban university College of Education (COE) program. Specific areas of interest included: (a) demographic characteristics profiling the study participants; (b) emotional and motivational factors as they affected the students; and (c) the personal thoughts and effect of institutional and environmental variables and administrative factors. Ten females volunteered to participate in a semi-structured interview. The 22 semi-structured interview questions were developed by the researcher. The questions captured the individual personal background, academic information, college environment, and reasons for leaving college. A triangulated set of research methods for data collection was used, including a demographic profile, the semi-structured interview, coding, and salient points and theme validation. Member checks and independent reviewers were used for verification and validation purposes.Conclusions drawn from this study include the findings that the majority of students were motivated to complete their degree, but a variety of barriers including personal problems, financial needs, faculty communication difficulties, lack of administrative support services, and isolation in classes existed. Some positive perceptions of the teacher education program included appreciation for most of the faculty in the college, technology services and the new facilities for the COE, and the existence of on-line advising capabilities. The following implications emerged from this study: (a) the COE needs a clear policy for recruiting at-risk African-American students; (b) flexible course selections and offerings conducive to non-traditional students are desirable; (c) a full-time recruiter to organize and facilitate student organization support is needed; (d) more African-American faculty are crucial as role models; and (e) it is essential to continue to focus on cultural awareness within the curriculum, and (f) creating a climate of support and togetherness in which students feel comfortable is necessary. Future research is recommended addressing the perceptions of at-risk African-American male students, other ethnic and racial minorities and other colleges within the university or across universities.

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Roughly half of all doctoral students who begin a program do not continue through graduation, and many of them face significant financial losses and emotional burdens as a result. Although this completion rate has stayed fairly constant for the past few decades, it has recently gained attention on a national level. In 2011, the National Research Council published the Assessment of Research Doctorate Programs in the United States, and provided a wealth of data on over 5,000 programs in 212 universities. This study used that dataset to examine the relationship between timely doctoral completion rates and 22 Program, Faculty and Student variables in the following five fields: Neuroscience, Chemical Engineering, Physics, Economics and English. The study also observed differences between programs with high completion rates and those with low completion rates in each field. The purpose of the study was to (1) determine which variables are significant in predicting doctoral completion rates, (2) discover if measurable differences exist between high and low completing programs, and (3) reveal the usefulness of collecting objective data in doctoral programs in order to assist doctoral programs as they create strategies to lower attrition rates. The sample in the study inculded over 10,000 students and over 12,000 faculty members from 365 programs in the five fields. The 22 variables in the study were: Availability of a Graduate Orientation, Existence of an Annual Student Review, Number of Academic Support Activities, Average First Year Enrollment Size, Total Number of Enrolled Students, Percentage of First Year Students with Full Financial Support, Percentage of Students that are Teaching Assistants, Percentage that are Research Assistants, Median Time to Degree, Average GRE Score, Percentage of Students that are Married, Percentage of Students with Dependents, Percentage of Students with Mentors, Average Satisfaction Rating, Average Sense of Belonging Rating, Percentage of Students that are Females, Percentage that are Minorities, Percentage of Faculty that are Females, Percentage that are Minorities, Percentage of Faculty with Grants, Total Number of Faculty, and Faculty to Student Ratio. All of the regression lines were significant at the p¡Ü.05 level. Furthermore, for Economics programs in the sample, 80%#37; of the variance in timely completion rates was explained by this specific set of variables, and the same set of variables explained between 40-66%#37; of the variance in timely completion rates for the other four fields in the study. When looking at all the programs in the dataset, the following variables were significantly related to timely completion rates: number of academic support activities, percentage of students with full financial support, 1st year size, annual student review, student satisfaction rating, number of faculty, percentage of students with teaching assistantships, percentage of faculty with grants, and time to degree. Between the high- and low-completion groups, the following variables were significantly different in the All Programs group: Student satisfaction rating, percentage of students with children, percentage of students with full financial support, number of academic support activities, time to degree, and percentage of students with teaching assistantships. Separate findings and implications are presented for each of the five fields (Neuroscience, Chemical Engineering, Physics, Economics and English). Program leaders and other interested parties can now use these results to focus their attention on significant variables as they create strategies for improving completion rates within their respective fields.

Distance learning is gaining popularity in many education environments. Online classes are on the increase because students need alternatives to traditional face-to-face classroom training. Time constraint on today's students coupled with the need for education to keep up with advances in technology, in all fields, has forced educators to facilitate training avenues other than traditional methods to perform this pedagogy. Students' perceptions of online classes along with differing learning styles are making this process challenging. Many colleges and universities are reporting alarmingly higher dropout rates of online students versus their traditional classroom students. There are many variables that can be attributed to this outcome and current research is looking at ways to increase the completion rate of this growing learning alternative. The purpose of this study is to examine some of the variables involved in the learning process that might have an effect on the online learning process. This may lead to a better understanding of why the completion rates are lower for online students. Some of the variables reviewed in this study are learning styles and technology acceptance, both of which can have an influence on the student's reception of the learning material. Online students scored higher in all but one of the technology acceptance scores. Learning styles showed a greater number of read/write learners in online classes and a greater number of kinesthetic learners in traditional classes. Instructors teaching styles also had a relationship with the students' successful completion of online classes.

Revenue accounting is a complex area of accounting with a lot of freedom for companies to adapt their accounting structures after its own needs. Depending on what the company in question regards as the purpose of a financial statement and how important that purpose is regarded the accounting structure will differ between companies. This study aims to examine which factors affect the structure of companies’ revenue accounting structure for continuing production. The purpose is to show how intricate balancing the different properties, principles and goals that are desired within revenue accounting for continuing production can be. The study was performed with a qualitative method and used semi-structured interviews regarding revenue accounting with the chief financial officers at seven different companies in the construction and consulting sector located within the Stockholm area. These interviews were later analyzed by means of accounting theories and research articles. The study concluded that the most crucial factor determining companies’ revenue accounting structure was accurate accounting both for internal and external use. Furthermore taxes, concern restriction or qualitative accounting properties had no effect. The elemental account principles could be seen to have had a certain influence. It could also be concluded that many companies prioritized a smooth revenue stream when deciding how to design their revenue accounting.

abstract: Video denoising has been an important task in many multimedia and computer vision applications. Recent developments in the matrix completion theory and emergence of new numerical methods which can efficiently solve the matrix completion problem have paved the way for exploration of new techniques for some classical image processing tasks. Recent literature shows that many computer vision and image processing problems can be solved by using the matrix completion theory. This thesis explores the application of matrix completion in video denoising. A state-of-the-art video denoising algorithm in which the denoising task is modeled as a matrix completion problem is chosen for detailed study. The contribution of this thesis lies in both providing extensive analysis to bridge the gap in existing literature on matrix completion frame work for video denoising and also in proposing some novel techniques to improve the performance of the chosen denoising algorithm. The chosen algorithm is implemented for thorough analysis. Experiments and discussions are presented to enable better understanding of the problem. Instability shown by the algorithm at some parameter values in a particular case of low levels of pure Gaussian noise is identified. Artifacts introduced in such cases are analyzed. A novel way of grouping structurally-relevant patches is proposed to improve the algorithm. Experiments show that this technique is useful, especially in videos containing high amounts of motion. Based on the observation that matrix completion is not suitable for denoising patches containing relatively low amount of image details, a framework is designed to separate patches corresponding to low structured regions from a noisy image. Experiments are conducted by not subjecting such patches to matrix completion, instead denoising such patches in a different way. The resulting improvement in performance suggests that denoising low structured patches does not require a complex method like matrix completion and in fact it is counter-productive to subject such patches to matrix completion. These results also indicate the inherent limitation of matrix completion to deal with cases in which noise dominates the structural properties of an image. A novel method for introducing priorities to the ranked patches in matrix completion is also presented. Results showed that this method yields improved performance in general. It is observed that the artifacts in presence of low levels of pure Gaussian noise appear differently after introducing priorities to the patches and the artifacts occur at a wider range of parameter values. Results and discussion suggesting future ways to explore this problem are also presented. / Dissertation/Thesis / M.S. Electrical Engineering 2013

abstract: Creative design lies at the intersection of novelty and technical feasibility. These objectives can be achieved through cycles of divergence (idea generation) and convergence (idea evaluation) in conceptual design. The focus of this thesis is on the latter aspect. The evaluation may involve any aspect of technical feasibility and may be desired at component, sub-system or full system level. Two issues that are considered in this work are: 1. Information about design ideas is incomplete, informal and sketchy 2. Designers often work at multiple levels; different aspects or subsystems may be at different levels of abstraction Thus, high fidelity analysis and simulation tools are not appropriate for this purpose. This thesis looks at the requirements for a simulation tool and how it could facilitate concept evaluation. The specific tasks reported in this thesis are: 1. The typical types of information available after an ideation session 2. The typical types of technical evaluations done in early stages 3. How to conduct low fidelity design evaluation given a well-defined feasibility question A computational tool for supporting idea evaluation was designed and implemented. It was assumed that the results of the ideation session are represented as a morphological chart and each entry is expressed as some combination of a sketch, text and references to physical effects and machine components. Approximately 110 physical effects were identified and represented in terms of algebraic equations, physical variables and a textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 16 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works. textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 15 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works. / Dissertation/Thesis / M.S. Mechanical Engineering 2014