Observations on the President’s Fiscal Year 2000 Federal Science and Technology Budget

http://deepblue.lib.umich.edu/bitstream/2027.42/89086/1/2000_FST_Budget_Analysis.pd

Observations on the President’s Fiscal Year 1999 Federal Science and Technology Budget

http://deepblue.lib.umich.edu/bitstream/2027.42/89085/1/1999_FST_Budget_Analysis.pd

Observations on the President’s Fiscal Year 2001 Federal Science and Technology Budget

http://deepblue.lib.umich.edu/bitstream/2027.42/89089/1/2001_FST_Budget_Analysis.pd

What’s in a Name?

Numerous concerns have been raised about the sustainability of the biomedical research enterprise in the United States. Improving the postdoctoral training experience is seen as a priority in addressing these concerns, but even identifying who the postdocs are is made difficult by the multitude of different job titles they can carry. Here, we summarize the detrimental effects that current employment structures have on training, compensation and benefits for postdocs, and argue that academic research institutions should standardize the categorization and treatment of postdocs. We also present brief case studies of two institutions that have addressed these challenges and can provide models for other institutions attempting to enhance their postdoctoral workforces and improve the sustainability of the biomedical research enterprise

Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

Developing a conceptual framework for an evaluation system for the NIAID HIV/AIDS clinical trials networks

Globally, health research organizations are called upon to re-examine their policies and practices to more efficiently and effectively address current scientific and social needs, as well as increasing public demands for accountability

Is Adding the E Enough?: Investigating the Impact of K-12 Engineering Standards on the Implementation of STEM Integration.

The problems that we face in our ever-changing, increasingly global society are multidisciplinary, and many require the integration of multiple science, technology, engineering, and mathematics (STEM) concepts to solve them. National calls for improvement of STEM education in the United States are driving changes in policy, particularly in academic standards. Research on STEM integration in K-12 classrooms has not kept pace with the sweeping policy changes in STEM education. This study addresses the need for research to explore the translation of broad, national-level policy statements regarding STEM education and integration to state-level policies and implementation in K-12 classrooms. An interpretive multicase study design was employed to conduct an in-depth investigation of secondary STEM teachers\u27 implementation of STEM integration in their classrooms during a yearlong professional development program. The interpretive approach was used because it provides holistic descriptions and explanations for the particular phenomenon, in this case STEM integration. The results of this study demonstrate the possibilities of policies that use state standards documents as a mechanism to integrate engineering into science standards. Our cases suggest that STEM integration can be implemented most successfully when mathematics and science teachers work together both in a single classroom (co-teaching) and in multiple classrooms (content teaching—common theme)

Entrepreneurship Assessment in Higher Education: A Research Review for Engineering Education Researchers

BackgroundDespite the wide adoption of entrepreneurship by United States engineering programs, there have been few advances in how to measure the influences of entrepreneurial education on engineering students. We believe the inadequate growth in engineering entrepreneurship assessment research is due to the limited use of research emerging from the broader entrepreneurship education assessment community.PurposeThis paper explores entrepreneurship education assessment by documenting the current state of the research and identifying the theories, variables, and research designs most commonly used by the broader community. We then examine if and how these theories and constructs are used in engineering entrepreneurship education.Scope/MethodTwo literature databases, Scopus® and Proquest, were searched systematically for entrepreneurship education assessment research literature. This search yielded 2,841 unique papers. Once inclusion and exclusion criteria were applied, 359 empirical research papers were coded for study design, theory, variables measured, instruments, and validity and reliability.ConclusionsWhile there has been growth in entrepreneurship education assessment research, little exchange of ideas across the disciplines of business, engineering, and education is occurring. Nonempirical descriptions of programs outweigh empirical research, and these empirical studies focus on affective, rather than cognitive or behavioral, outcomes. This pattern within the larger entrepreneurship community is mirrored in engineering where the use of theoryâ based, validated entrepreneurship education assessment instruments generally focuses on the context of intent to start a new company. Given the engineering community’s goals to support engineering entrepreneurship beyond business creation, the engineering education community should consider developing assessment instruments based in theory and focused on engineeringâ specific entrepreneurship outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145556/1/jee20197.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145556/2/jee20197_am.pd

Engineering PhD Returners and Direct‐Pathway Students: Comparing Expectancy, Value, and Cost

BackgroundProfessionals who pursue a doctorate after significant post‐baccalaureate work experience, a group we refer to as returners, represent an important but understudied group of engineering doctoral students. Returners are well situated to leverage their applied work experiences in their advanced engineering training.Purpose/HypothesisWe drew on results from the Graduate Student Experiences and Motivations Survey to explore the dimensionality of our scales measuring value and cost constructs. We used these scales, as well as measures of student expectancy of success, to compare returners with direct‐pathway students.Design/MethodWe surveyed 179 returners and 297 direct‐pathway domestic engineering doctoral students. We first conducted Exploratory Factor Analysis on our cost and value measures. We then used both Ordinary Least Squares and Ordinal Regression Model analyses to assess the relationships of various student characteristics and experiences (including returner status) with student expectancy of success and the emergent cost and values factors associated with doctoral study in engineering.ResultsFactor analysis revealed three categories of values (interest, attainment, and career utility) that were largely consistent with those in Eccles’ expectancy‐value framework. A similar analysis identified three categories of costs (balance, financial, and academic) associated with pursuing a PhD. Returners felt significantly less confident in their ability to complete their degrees prior to enrolling and perceived higher levels of all cost types than direct‐pathway students.ConclusionsGiven the differences between returning and direct‐pathway students, it is important to consider how universities might best recruit and retain returners. Tracking returner status could be critical in better supporting these students.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140046/1/jee20182.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140046/2/jee20182_am.pd