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

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

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

The American Science Pipeline: Sustaining Innovation in a Time of Economic Crisis

Significant limitations have emerged in America's science training pipeline, including inaccessibility, inflexibility, financial limitations, and lack of diversity. We present three effective programs that collectively address these challenges. The programs are grounded in rigorous science and integrate through diverse disciplines across undergraduate, graduate, and postdoctoral students, and resonate with the broader community. We discuss these models in the context of current economic constraints on higher education and the urgent need for our institutions to recruit and retain diverse student populations and sustain the successful American record in scientific education and innovation

A suggested framework and guidelines for learning GIS in interdisciplinary research

Interdisciplinary research with geographic information systems (GIS) can be rewarding as researchers from different disciplines have the opportunity to create something novel. GIS, though, is known to be difficult to use and learn. It is imperative for its successful use in projects that those who need to use GIS are able to learn it quickly and easily. To better support interdisciplinary research with GIS, it is necessary to understand what researchers with interdisciplinary experience wanted to use it for and how they learned it. The aim would be to advise geography educators on creating learning resources that could compliment or supplement existing learning approaches used by interdisciplinary researchers to improve the learning experience and uptake of GIS. This article explores the results from an online survey and interviews conducted between July 2014 and August 2015 with participants from the UK, the US and Europe on how interdisciplinary researchers learned GIS and which resources and platforms were utilised. Guidelines and a framework are presented, modifying the Technological Pedagogical and Content Knowledge framework, incorporating informal and context-based learning and GIS concepts from the Geographic Information Science and Technology Body of Knowledge. Findings show that interdisciplinary researchers want to use GIS to capture, analyse and visualise information; they largely use informal learning approaches (e.g. internet searches, watching a video, ask a more experienced person); and they predominantly use ArcGIS, QGIS and web GIS platforms. Future work suggests resources use contextually relevant learning activities and bear in mind nuances of disciplinary language

Identifying a Typology of High Schools Based on Their Orientation Toward STEM: A Latent Class Analysis of HSLS:09

The purpose of this study is to investigate the extent that there is a typology of high schools based on their orientation toward STEM, as well as the extent to which school-level demographic variables and student high school outcomes are associated with subgroup membership in the typology, by analyzing data from a large nationally representative sample of high schools (n=940) from the High School Longitudinal Study of 2009 (HSLS:09) using latent class analysis (LCA). We used a three-step LCA approach to identify significantly different subgroups of STEM-oriented high schools, what covariates predict subgroup membership, and how subgroup membership predicts observed distal outcomes. We find that there are four significantly different subgroups of STEM-oriented high schools based on their principal’s perceptions: Abundant (12.3%), Support (23.3%), Bounded (10.1%), and Comprehensive (54.3%). In addition, we find that these subgroups are associated with school demographics, such as the percent of students eligible for free and reduced-price lunch, school locale, and control (public or private). Subgroup membership is also associated with student outcomes, such as postsecondary program enrollment and intent to pursue a STEM degree. Keywords: STEM Education, High Schools, Multivariate Analysi

Creating an Instrument to Measure Student Response to Instructional Practices

BackgroundCalls for the reform of education in science, technology, engineering, and mathematics (STEM) have inspired many instructional innovations, some research based. Yet adoption of such instruction has been slow. Research has suggested that students’ response may significantly affect an instructor’s willingness to adopt different types of instruction.PurposeWe created the Student Response to Instructional Practices (StRIP) instrument to measure the effects of several variables on student response to instructional practices. We discuss the step‐by‐step process for creating this instrument.Design/MethodThe development process had six steps: item generation and construct development, validity testing, implementation, exploratory factor analysis, confirmatory factor analysis, and instrument modification and replication. We discuss pilot testing of the initial instrument, construct development, and validation using exploratory and confirmatory factor analyses.ResultsThis process produced 47 items measuring three parts of our framework. Types of instruction separated into four factors (interactive, constructive, active, and passive); strategies for using in‐class activities into two factors (explanation and facilitation); and student responses to instruction into five factors (value, positivity, participation, distraction, and evaluation).ConclusionsWe describe the design process and final results for our instrument, a useful tool for understanding the relationship between type of instruction and students’ response.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/1/jee20162_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/2/jee20162.pd