Patron Driven Acquisition (PDA) is a growing method of collection development in academic libraries that follows a Just-In-Time model versus the more traditional Just-In-Case model. Arizona State University (ASU) implemented our current PDA plan and profiles in 2009 with minimal changes occurring since this initial implementation date. Our PDA model of collection development involves purchasing print and e-books when users select them in the online catalog, rather than receiving items on an approval plan or by librarian selection. After an initial investigation concluded that several major health sciences publications had not been loaded into the catalog for potential patron selection, we began a more thorough examination of our PDA profile.

ASU serves over 6,500 students, faculty, and staff in Nursing and Allied Health fields in a range of programs requiring a robust collection. This poster details the process we used to determine whether the profiles created by previous librarians in 2009 have succeeded in uploading records for publications that appear on the 2014 nursing texts from Doody’s Core Titles into our catalog. Specifically, our poster will present on the number of Doody’s titles that were excluded from the PDA plan due to our profile settings and analyze why these titles were excluded. Our findings will allow us to order titles that are currently missing from our collection as well as tailor our PDA profiles to include key texts in nursing and allied health subjects in the future. We will also provide recommendations and considerations for other libraries considering or using a PDA model for purchasing texts in the health sciences.

This is a brief text intended for use in undergraduate school-and-society classes. Your class may also be titled “Social foundations of education.” “Social foundations of education” is an interdisciplinary field that includes both humanities and social-science perspectives on schooling. It thus includes study of the philosophy and history of education as well as sociological, economic, anthropological, and political perspectives on schooling.

The core of most social foundations classes lies in the relationship between formal schooling and broader society. This emphasis means that while some parts of psychology may be related to the core issues of social foundations classes—primarily social psychology—the questions that are asked within a social-foundations class are different from the questions raised in child development, educational psychology, and most teaching-methods classes. For example, after finishing the first chapter of this text, you should be able to answer the question, “Why does the federal government pay public schools to feed poor students at breakfast and lunch?” Though there is some psychology research tying nutrition to behavior and learning, the policy is based on much broader expectations of schools. In this case, “Children learn better if they are well-fed” both is based on research and also is an incomplete answer.

In the wake of both the end of court-ordered school desegregation and the growing popularity of accountability as a mechanism to maximize student achievement, the authors explore the association between racial segregation and the percentage of students passing high-stakes tests in Florida's schools. Results suggest that segregation matters in predicting school-level performance on the Florida Comprehensive Assessment Test after control for other known and purported predictors of standardized test performance. Also, these results suggest that neither recent efforts by the state of Florida to equalize the funding of education nor current efforts involving high-stakes testing will close the Black-White achievement gap without consideration of the racial distribution of students across schools.

The recent battle reported from Washington about proposed national testing program does not tell the most important political story about high stakes tests. Politically popular school accountability systems in many states already revolve around statistical results of testing with high-stakes environments. The future of high stakes tests thus does not depend on what happens on Capitol Hill. Rather, the existence of tests depends largely on the political culture of published test results. Most critics of high-stakes testing do not talk about that culture, however. They typically focus on the practice legacy of testing, the ways in which testing creates perverse incentives against good teaching.

More important may be the political legacy, or how testing defines legitimate discussion about school politics. The consequence of statistical accountability systems will be the narrowing of purpose for schools, impatience with reform, and the continuing erosion of political support for publicly funded schools. Dissent from the high-stakes accountability regime that has developed around standardized testing, including proposals for professionalism and performance assessment, commonly fails to consider these political legacies. Alternatives to standardized testing which do not also connect schooling with the public at large will not be politically viable.

This paper presents a Bayesian framework for evaluative classification. Current education policy debates center on arguments about whether and how to use student test score data in school and personnel evaluation. Proponents of such use argue that refusing to use data violates both the public’s need to hold schools accountable when they use taxpayer dollars and students’ right to educational opportunities. Opponents of formulaic use of test-score data argue that most standardized test data is susceptible to fatal technical flaws, is a partial picture of student achievement, and leads to behavior that corrupts the measures.

A Bayesian perspective on summative ordinal classification is a possible framework for combining quantitative outcome data for students with the qualitative types of evaluation that critics of high-stakes testing advocate. This paper describes the key characteristics of a Bayesian perspective on classification, describes a method to translate a naïve Bayesian classifier into a point-based system for evaluation, and draws conclusions from the comparison on the construction of algorithmic (including point-based) systems that could capture the political and practical benefits of a Bayesian approach. The most important practical conclusion is that point-based systems with fixed components and weights cannot capture the dynamic and political benefits of a reciprocal relationship between professional judgment and quantitative student outcome data.

The current debate over graduate rate calculations and results has glossed over the relationship between student migration and the accuracy of various graduation rates proposed over the past five years. Three general grade-based graduation rates have been proposed recently, and each has a parallel version that includes an adjustment for migration, whether international, internal to the U.S., or between different school sectors. All of the adjustment factors have a similar form, allowing simulation of estimates from real data, assuming different unmeasured net migration rates. In addition, a new age-based graduation rate, based on mathematical demography, allows the simulation of estimates on a parallel basis using data from Virginia's public schools.

Both the direct analysis and simulation demonstrate that graduation rates can only be useful with accurate information about student migration. A discussion of Florida's experiences with longitudinal cohort graduation rates highlights some of the difficulties with the current status of the oldest state databases and the need for both technical confidence and definitional clarity. Meeting the No Child Left Behind mandates for school-level graduation rates requires confirmation of transfers and an audit of any state system for accuracy, and basing graduation rates on age would be a significant improvement over rates calculated using grade-based data.