以正当程序实现教育公平

教育公平是教育的一种基本价值观念与准则,与教育制度相关联,并以此为基准规定着社会成员所享有的基本教育权利,规定着教育资源与利益在社会成员之间的合理分配。事实证明,即使是在机会平等的条件下,教育权利的行使也往往因为主体自然禀赋的差异而不尽相同。为此,我们需在充分认识正当程序对实现教育公平的价值基础上,努力建构合理的程序,为教育资源分配的正义提供程序规则依据;充分运用"商谈理论",为解决教育的公平问题提供民主化程序基础和救济机制。     

Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers

In this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey (DIT). The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program (JASP), and thresholding based on Bayes Factors (BF), to scholars in the field of moral education.     

Why We (Usually) Don't Have to Worry About Multiple Comparisons

Abstract

Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern.     

Characterizing the Experimental Procedure in Science Laboratories: A preliminary step towards students experimental design

Many studies have stressed students’ lack of understanding of experiments in laboratories. Some researchers suggest that if students design all or parts of entire experiment, as part of an inquiry-based approach, it would overcome certain difficulties. It requires that a procedure be written for experimental design. The aim of this paper is to describe the characteristics of a procedure in science laboratories, in an educational context. As a starting point, this paper proposes a model in the form of a hierarchical task diagram that gives the general structure of any procedure. This model allows both the analysis of existing procedures and the design of a new inquiry-based approach. The obtained characteristics are further organized into criteria that can help both teachers and students assess a procedure during and after its writing. These results are obtained through two different sets of data. First, the characteristics of procedures are established by analysing laboratory manuals. This allows the organization and type of information in procedures to be defined. This analysis reveals that students are seldom asked to write a full procedure, but sometimes have to specify tasks within a procedure. Secondly, iterative interviews are undertaken with teachers. This leads to the list of criteria to evaluate the procedure.     

Procedures for Selecting Items for Computerized Adaptive Tests

Many procedures have been developed for selecting the "best" items for a computerized adaptive test. There is a trend toward the use of adaptive testing in applied settings such as licensure tests, program entrance tests, and educational tests. It is useful to consider procedures for item selection and the special needs of applied testing settings to facilitate test design. The current study reviews several classical approaches and alternative approaches to item selection and discusses their relative merit. This study also describes procedures for constrained computerized adaptive testing (C-CAT) that may be added to classical item selection approaches to allow them to be used for applied testing, while maintaining the high measurement precision and short test length that made adaptive testing attractive to practitioners initially.     

Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998-2001

Objective： To estimate the prevalence rates of neural tube defects （NTDs） in Heshun County, Shanxi Province, China by Bayesian smoothing technique. Methods： A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results： The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions： The Bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.     

Generalizability Theory

Generalizability theory consists of a conceptual framework and a methodology that enable an investigator to disentangle multiple sources of error in a measurement procedure. The roots of generalizability theory can be found in classical test theory and analysis of variance (ANOVA), but generalizability theory is not simply the conjunction of classical theory and ANOVA. In particular, the conceptual framework in generalizability theory is unique. This framework and the procedures of generalizability theory are introduced and illustrated in this instructional module using a hypothetical scenario involving writing proficiency.     

The risk of delayed school entry in relation to migration background in different institutional settings

In Germany, children of immigrants are faced with delayed school entry about twice as often as children of native-born parents because they are more often diagnosed not being ready for school. We investigate these early educational inequalities, focusing on the interplay of individual competencies and the institutional context in terms of the school enrollment procedure. We compare the conventional procedure, where school enrollment is supposed to be bound on school readiness, with reformed procedures where school enrollment is not supposed to be bound on school readiness. Data of school entry examinations from Hesse, Germany from 2008–2012 are analyzed (N = 50,717). The results show that children of immigrants from all origin groups have a substantially higher risk of obtaining a recommendation for delayed school entry than children of native-born parents. However, these ethnic differences are strongly reduced in the reformed procedures as compared to the conventional school enrollment procedure, indicating that these procedures could be an effective measure to reduce ethnic educational inequalities at school start.     

Modeling Diagnostic Assessments with Bayesian Networks

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models are reviewed, as they affect applications to diagnostic assessment. The paper discusses how Bayesian network models are set up with expert information, improved and calibrated from data, and deployed as evidence-based inference engines. Aimed at a general educational measurement audience, the paper illustrates the flexibility and capabilities of Bayesian networks through a series of concrete examples, and without extensive technical detail. Examples are provided of proficiency spaces with direct dependencies among proficiency nodes, and of customized evidence models for complex tasks. This paper is intended to motivate educational measurement practitioners to learn more about Bayesian networks from the research literature, to acquire readily available Bayesian network software, to perform studies with real and simulated data sets, and to look for opportunities in educational settings that may benefit from diagnostic assessment fueled by Bayesian network modeling.     

Effects of Prereading Story Mapping on Elementary Readers' Comprehension

Abstract

In this study, the effects of two teacher-directed prereading instructional procedures (directed reading activity [DRA] vs. story grammar/structured overview story mapping) on literal and inferential reading comprehension were compared. Sixty third-grade and 60 fifth-grade students were randomly assigned to treatment groups, and data were analyzed within grade levels by an analysis of variance. The prereading modified story mapping procedure resulted in 14% better inferential comprehension (p < .0005) and 7% better literal comprehension than did DRA at the third-grade level. Statistically significant differences between groups were not found at the fifth-grade level. Implications for practice and theory are suggested.     

APPLICATION OF COMPUTERIZED ADAPTIVE TESTING TO EDUCATIONAL PROBLEMS

Three applications of computerized adaptive testing (CAT) to help solve problems encountered in educational settings are described and discussed. Each of these applications makes use of item response theory to select test questions from an item pool to estimate a student's achievement level and its precision. These estimates may then be used in conjunction with certain testing strategies to facilitate certain educational decisions. The three applications considered are (a) adaptive mastery testing for determining whether or not a student has mastered a particular content area, (b) adaptive grading for assigning grades to students, and (c) adaptive self-referenced testing for estimating change in a student's achievement level. Differences between currently used classroom procedures and these CAT procedures are discussed. For the adaptive mastery testing procedure, evidence from a series of studies comparing conventional and adaptive testing procedures is presented showing that the adaptive procedure results in more accurate mastery classifications than do conventional mastery tests, while using fewer test questions.     

科尔曼报告与中国教育装备史研究

从科尔曼研究报告的结论出发,对教育装备在教育均衡和学生学业中的作用问题提出研究需求方面的思考。指出科尔曼报告的局限性,并认为可以通过中国教育装备发展历史的研究来诠释和解决这一问题。     

Implications of Item Response Theory for the Measurement Practitioner

Over the past several years, item response theory (IRT) has had a dramatic impact on the field of educational measurement. At the theoretical level and for educational researchers, it has shown itself to have certain advantages over classical theory both in terms of the research questions it can address and in terms of the applications it can support. For these benefits to be realized by the measurement practitioner, however, two major difficulties must be confronted: (a) the enormous complexity of IRT theory and procedures and (b) the lack of robustness of IRT procedures to violation of assumptions.     

Wisdom and Care as the Two Faces of Educational Action

The central aim of this paper is to explore a kind of rationality that seeks to do justice to educational practice: to our understanding of its complexities and to its actual conduct. The enquiry begins with a review of the limits of standardising procedures, including their inbuilt technicist bias, which are increasingly dominant in educational policymaking internationally. Secondly, the classical Aristotelian idea of practical rationality is invoked (with the central virtue phronesis) because it offers a genuine moral point of view in conceiving educational activity, even if it is acknowledged that it is insufficient in describing the complexity of educational activity itself. Thirdly, the argument then proceeds by introducing two bi-dimensional concepts; concepts, it is argued, that can give a fuller account of and a more adequate rationale for educational action. The first bi-dimensional concept draws on Aristotelian argument to elucidate a notion of educational action (not just moral action) with the couple epimeleia-phronesis (care-wisdom). The second bi-dimensional concept recasts our understanding of temporality in education as an interplay of Kronos and Kairos—of chronological time and kairological time.     

Inferential statistics and the use of administrative data in US educational research

While the use of inferential statistics is a nearly universal practice in the social sciences, there are instances where its application is unnecessary and potentially misleading. This is true for a portion of research using administrative data in educational research in the United States. Surveying all research articles using administrative data published in Educational Researcher and American Educational Research Journal between 2011 and 2013, we find that over half use inferential techniques to help interpret results despite the use of population data. To the extent that these practices and findings are replicated and interpreted in the United States and elsewhere, this can lead to the undervaluing of potentially meaningful patterns and trends.     

Mathematics education and learning disabilities in Spain

In the first part of this article, we describe the basic objectives of the math curriculum in Spain as well as the basic contents, teacher resources, and obstacles perceived in mathematics instruction. Second, we briefly describe the concept of learning disabilities (LD) as they are currently defined in Spain. As stated in the recent educational reform, a student with LD is any student with special educational needs. The emphasis is placed on the educational resources that these students need in order to achieve the curricular objectives that correspond to their age group or grade. Third, we comment specifically on the educational services model and the evaluation and instructional procedures for students with math learning disabilities. Finally, we describe some lines of research that have appeared in the last few years in Spain that have led to the development of new evaluation and intervention procedures for students with LD in computation and problem solving.     

Principles of evolutionary educational psychology

Evolutionary educational psychology is the study of the relation between evolved systems of folk knowledge and inferential and attributional biases as these relate to academic learning in modern society. Following discussion and illustration of the mechanisms of natural selection and their application to human motivational, cognitive, and behavioral evolution, the basic premises and principles of evolutionary educational psychology are outlined. The gist is that the evolved cognitive systems and inferential biases that define folk knowledge are not sufficient for academic learning, but, at the same time, are the foundation from which academic competencies are built. A theoretical frame outlining the relation between folk knowledge and academic development is proposed and implications for motivational issues and instructional practices are detailed.