Faculty and Staff

In spring 2013, the presenters developed a survey on academic library streaming video and distributed it broadly through various discussion and mailing lists.

This is the first large-scale and most comprehensive effort to date to collect data on streaming video funding, licensing, acquisition, and hosting in academic libraries. Its results will provide benchmark data for future explorations of this rapidly expanding approach to video in academic libraries.

Streaming video is becoming a common occurrence on many campuses today. Its fast growth is due in part to the steady growth of online classes and programs. Technology has also played a role in this growth as alternatives for ingesting and accessing content have expanded. Multiple options are now available including in-house approaches, cloud storage, and third party vendors.

This survey collected data on how academic institutions address the day-to-day operations related to streaming video as well as perceived directions for future action.

Survey questions addressed selection and acquisition of video in both hard copy and streaming formats, funding for acquisitions, current and planned hosting interfaces, cataloging and access, and current practice and policy on digitization of hard copy titles for streaming. This session reviews the instrument used, and provides a preliminary look at some of the key data collected.

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.

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.