Experience and Educationis the best concise statement on education ever published by John Dewey, the man acknowledged to be the pre-eminent educational theorist of the twentieth century. Written more than two decades after Democracy and Education(Dewey's most comprehensive statement of his position in educational philosophy), this book demonstrates how Dewey reformulated his ideas as a result of his intervening experience with the progressive schools and in the light of the criticisms his theories had received. Analysing both "traditional" and "progressive" education, Dr. Dewey here insists that neither the old nor the new education is adequate and that each is miseducative because neither of them applies the principles of a carefully developed philosophy of experience. Many pages of this volume illustrate Dr. Dewey's ideas for a philosophy of experience and its relation to education. He particularly urges that all teachers and educators looking for a new movement in education should think in terms of the deeped and larger issues of education rather than in terms of some divisive "ism" about education, even such an "ism" as "progressivism." His philosophy, here expressed in its most essential, most readable form, predicates an American educational system that respects all sources of experience, on that offers a true learning situation that is both historical and social, both orderly and dynamic.

经验与教育 = Experience and education

Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan

https://www.grips.ac.jp/r-center/wp-content/uploads/07-08.pdf

Network DEA: A slacks-based measure approach

A club is a voluntary group of individuals who derive mutual benefit from sharing one or more of the following: production costs, the members' characteristics, or a good characterized by excludable benefits. When production costs are shared and the good is purely private, a private good club is being analyzed (McGuire 1972; Wiseman 1957). If membership characteristics differ and motivate sharing, then membership fees will differ among members (DeSerpa 1977; Scotchmer 1994b; Scotchmer and Wooders 1987). Such fees are nonanonymous , inasmuch as a fee structure is related to the identity and attributes of a member. The focus of our analysis is the sharing of an excludable (rivalrous) public good, which we term a club good . Unless otherwise specified, crowding is assumed to be independent of the individual and hence anonymous. A number of aspects of the club definition deserve highlighting. Privately owned and operated clubs must be voluntary; members choose to belong because they anticipate a net benefit from membership. Thus, the utility jointly derived from membership and from the consumption of other goods must exceed the utility associated with nonmembership status. Furthermore, the net gain in utility from membership must exceed or equal membership fees or toll payments. This voluntarism serves as the first characteristic by which to distinguish between pure public goods and club goods. In the case of a pure public good, voluntarism may be absent, since the good might harm some recipients (e.g., defense to a pacifist, fluoridation to someone who opposes its use).

The theory of externalities, public goods, and club goods: Homogeneous clubs and local public goods

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

Data envelopment analysis 1978-2010: A citation-based literature survey

Ambulatory surgery centers (ASCs) provide a low-cost alternative to traditional inpatient care In addition, with health care reform imminent, it is likely that many currently uninsured people will soon acquire health care coverage, significantly increasing the demand for health services ASCs are among the providers that can expect to see a substantial amount of this new pent-up demand and, therefore, ASCs are likely to continue their current growth into the foreseeable future Those ASCs that plan accordingly by optimizing procedure mix and volume will benefit most from the increased demand We propose a two-stage efficiency-based multicriteria decision model to guide an ASC in identifying its optimal procedure mix The first stage uses Data Envelopment Analysis (DEA) to calculate the efficiency of each procedure based on the resources required to perform the procedure, the revenue it generates, and its risk of complications The second stage uses the DEA factor efficiency scores in a bottleneck program to optimize the mix of procedures while satisfying the ASC's resource and operational constraints The criteria are to (1) maximize reimbursement while (2) minimizing the total number of complications We demonstrate the approach using a data set based in part on data from an actual ASC

An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers

Data envelopment analysis (DEA) assumes that inputs and outputs are measured on scales in which larger numerical values correspond to greater consumption of inputs and greater production of outputs. We present a class of DEA problems in which one or more of the inputs or outputs are naturally measured on scales in which higher numerical values represent lower input consumption or lower output production. We refer to such quantities as reverse inputs and reverse outputs. We propose to incorporate reverse inputs and outputs into a DEA model by returning to the basic principles that lead to the DEA model formulation. We compare our method to reverse scoring, the most commonly used approach, and demonstrate the relative advantages of our proposed technique. We use this concept to analyze all 30 Major League Baseball (MLB) organizations during the 1999 regular season to determine their on-field and front office relative efficiencies. Our on-field DEA model employs one output and two symmetrically defined inputs, one to measure offense and one to measure defense. The defensive measure is such that larger values correspond to worse defensive performance, rather than better, and hence is a reverse input. The front office model uses one input. Its outputs, one of which is a reverse output, are the inputs to the on-field model. We discuss the organizational implications of our results.

Data Envelopment Analysis with Reverse Inputs

Many real-world problems require decision makers to consider multiple criteria when performing an analysis. One popular method used to analyze multicriteria decision problems is goal programming. When applying goal programming, it is often difficult if not impossible to determine the target values and unit penalty weights with any level of confidence. Thus, in many situations, managers and decision makers may be forced to specify these parameters subjectively. In this paper, we present a model framework designed to eliminate the arbitrary assignment of target values and unit penalty weights when applying goal programming to solve multicriteria decision problems. In particular, when neither of these parameters is available, we show how to integrate factor efficiency scores determined from data envelopment analysis into the model. We discuss an application of the methodology to ambulatory surgery centers and demonstrate the model framework via a product mix example.

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Using DEA Factor Efficiency Scores to Eliminate Subjectivity in Goal Programming

Governments at all levels face budget shortfalls, and consequently public transit systems in the U.S. must compete with other public services for financial support. In order to depend less on public funding, it is critical that public transit systems focus on their operational performance and identify any sources of inefficiency. In this chapter, we present an unoriented network DEA methodology that measures a public transit system’s performance relative to its peer systems, compares its performance to an appropriate efficient benchmark system, and identifies the sources of its inefficiency.

Measuring and Managing the Productivity of U.S. Public Transit Systems: An Unoriented Network DEA

Data envelopment analysis (DEA) has been extensively applied to measure the performance of individual athletes and teams in a variety of sports as well as to analyze nations competing in the Olympics. Most of the models presented in the literature are single-stage DEA models which treat the underlying process of converting inputs into outputs as a “black box.” In many situations, analysts are interested in investigating the sources of inefficiency within the organization in order to improve organizational performance. To accomplish this, researchers have developed two-stage and network DEA methodologies.

Herbert F. Lewis

Papers

An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers

Data Envelopment Analysis with Reverse Inputs

Using DEA Factor Efficiency Scores to Eliminate Subjectivity in Goal Programming

Measuring and Managing the Productivity of U.S. Public Transit Systems: An Unoriented Network DEA

Performance Measurement of Major League Baseball Teams Using Network DEA