A Proof of the Duality of the DINA Model and the DINO Model

The Deterministic Input Noisy Output “AND” gate (DINA) model and the Deterministic Input Noisy Output “OR” gate (DINO) model are two popular cognitive diagnosis models (CDMs) for educational assessment. They represent different views on how the mastery of cognitive skills and the probability of a correct item response are related. Recently, however, Liu, Xu, and Ying demonstrated that the DINO model and the DINA model share a “dual” relation. This means that one model can be expressed in terms of the other, and which of the two models is fitted to a given data set is essentially irrelevant because the results are identical. In this article, a proof of the duality of the DINA model and the DINO model is presented that is tailored to the form and parameterization of general CDMs that have become the new theoretical standard in cognitively diagnostic modeling.     

A Nonparametric Approach to Cognitive Diagnosis by Proximity to Ideal Response Patterns

A trend in educational testing is to go beyond unidimensional scoring and provide a more complete profile of skills that have been mastered and those that have not. To achieve this, cognitive diagnosis models have been developed that can be viewed as restricted latent class models. Diagnosis of class membership is the statistical objective of these models. As an alternative to latent class modeling, a nonparametric procedure is introduced that only requires specification of an item-by-attribute association matrix, and classifies according to minimizing a distance measure between observed responses, and the ideal response for a given attribute profile that would be implied by the item-by-attribute association matrix. This procedure requires no statistical parameter estimation, and can be used on a sample size as small as 1. Heuristic arguments are given for why the nonparametric procedure should be effective under various possible cognitive diagnosis models for data generation. Simulation studies compare classification rates with parametric models, and consider a variety of distance measures, data generation models, and the effects of model misspecification. A real data example is provided with an analysis of agreement between the nonparametric method and parametric approaches.     

Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models

Multiple choice items on tests and Likert items on surveys are ubiquitous in educational, social and behavioral science research; however, methods for analyzing of such data can be problematic. Multidimensional item response theory models are proposed that yield structured Poisson regression models for the joint distribution of responses to items. The methodology presented here extends the approach described in Anderson, Verkuilen, and Peyton (2010) that used fully conditionally specified multinomial logistic regression models as item response functions. In this paper, covariates are added as predictors of the latent variables along with covariates as predictors of location parameters. Furthermore, the models presented here incorporate ordinal information of the response options thus allowing an empirical examination of assumptions regarding the ordering and the estimation of optimal scoring of the response options. To illustrate the methodology and flexibility of the models, data from a study on aggression in middle school (Espelage, Holt, and Henkel 2004) is analyzed. The models are fit to data using SAS.     

Using Neural Network Analysis to Define Methods of DINA Model Estimation for Small Sample Sizes

The DINA model is a commonly used model for obtaining diagnostic information. Like many other Diagnostic Classification Models (DCMs), it can require a large sample size to obtain reliable item and examinee parameter estimation. Neural Network (NN) analysis is a classification method that uses a training dataset for calibration. As a result, if this training dataset is determined theoretically, as was the case in Gierl’s attribute hierarchical method (AHM), the NN analysis does not have any sample size requirements. However, a NN approach does not provide traditional item parameters of a DCM or allow for item responses to influence test calibration. In this paper, the NN approach will be implemented for the DINA model estimation to explore its effectiveness as a classification method beyond its use in AHM. The accuracy of the NN approach across different sample sizes, item quality and Q-matrix complexity is described in the DINA model context. Then, a Markov Chain Monte Carlo (MCMC) estimation algorithm and Joint Maximum Likelihood Estimation is used to extend the NN approach so that item parameters associated with the DINA model are obtained while allowing examinee responses to influence the test calibration. The results derived by the NN, the combination of MCMC and NN (NN MCMC) and the combination of JMLE and NN are compared with that of the well-established Hierarchical MCMC procedure and JMLE with a uniform prior on the attribute profile to illustrate their strength and weakness.     

Estimation of Generalized DINA Model with Order Restrictions

Cognitive diagnostic models provide valuable information on whether a student has mastered each of the attributes a test intends to evaluate. Despite its generality, the generalized DINA model allows for the possibility of lower correct rates for students who master more attributes than those who know less. This paper considers the use of order-constrained parameter space of the G-DINA model to avoid such a counter-intuitive phenomenon and proposes two algorithms, the upward and downward methods, for parameter estimation. Through simulation studies, we compare the accuracy in parameter estimation and in classification of attribute patterns obtained from the proposed two algorithms and the current approach when the restricted parameter space is true. Our results show that the upward method performs the best among the three, and therefore it is recommended for estimation, regardless of the distribution of respondents’ attribute patterns, types of test items, and the sample size of the data.     

技术传统与技术哲学视界

本文提出了一个理解和描述各种传统技术的基本概念——技术传统。技术传统的最直接含义是一个技术能够存在的最小单位，由一个操作规则系统、一套工艺学知识所构成，同时还要有稳定的可传习性。技术传统构成着我们人类的生存方式。在我们生存方式的构成中技术传统不仅是一个技能共同体，而且还是生存共同体，它所完成的是文明的伦理功能，是支撑德性社会的世俗基础。考察技术传统能够给知识、技术、世界、生存等概念带来富有意义的变化。     

A mixture likelihood approach for generalized linear models

A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this approach handles many of the existing latent class regression procedures as special cases, as well as a host of other parametric specifications in the exponential family heretofore not mentioned in the latent class literature. As such we generalize the McCullagh and Nelder approach to a latent class framework. The parameters are estimated using maximum likelihood, and an EM algorithm for estimation is provided. A Monte Carlo study of the performance of the algorithm for several distributions is provided, and the model is illustrated in two empirical applications.     

科学知识变化的三种解释模式及其评价

科学知识的发展变化问题是20世纪科学哲学所关注的核心论题之一。纵观20世纪科学哲学的发展，主要地形成了三种解释模式：逻辑一理性论模式、认知论模式和社会学模式。虽然在20世纪的最后30年认知主义者和科学知识社会学家都对逻辑一理性论进行了猛烈的批判，但它们也未能对科学知识的变化问题给出令人满意的说明。对科学知识发展变化的完全说明应是这三种模式的有机统一。     

On the Identifiability in the Latent Budget Model

end-member model . A major drawback of the latent budget model is that, in general, the model is not identifiable, which complicates the interpretation of the model considerably. This paper studies the geometry and identifiability of the latent budget model. Knowledge of the geometric structure of the model is used to specify an appropriate criterion to identify the model. The results are illustrated by an empirical data set.     

团队心智模型和交互记忆系统：两种团队知识表征方式

团队是知识创造和创新的主体,其认知特征和管理模式越来越引起人们的关注。团队认知与管理的基础在于团队知识的表征方式。在认知科学的视角下,团队心智模型和交互记忆系统这两种主要的团队知识表征方式有其特定的概念内涵,它们之间存在复杂的内在联系。在研究团队行为时需要越过具体的行为本身,探究团队知识表征的内在逻辑和意义。     

浅议国际商务合同翻译教学中术语意识的培养

国际商务合同翻译过程中涉及多个专业的术语翻译,如法律术语、国际贸易术语、保险术语和金融术语等。作者认为在国际商务合同翻译教学中除了讲授国际商务合同的基础知识、语言特点、句法结构和合同的整体构成以及普通翻译技巧等之外,还需要重视学生对术语基础知识和术语翻译原则等方面的学习,培养学生的术语意识,提升学生的国际商务合同翻译质量和水平,并给出了培养学生术语意识的相关方法。     

Constrained Multilevel Latent Class Models for the Analysis of Three-Way Three-Mode Binary Data

Probabilistic feature models (PFMs) can be used to explain binary rater judgements about the associations between two types of elements (e.g., objects and attributes) on the basis of binary latent features. In particular, to explain observed object-attribute associations PFMs assume that respondents classify both objects and attributes with respect to a, usually small, number of binary latent features, and that the observed object-attribute association is derived as a specific mapping of these classifications. Standard PFMs assume that the object-attribute association probability is the same according to all respondents, and that all observations are statistically independent. As both assumptions may be unrealistic, a multilevel latent class extension of PFMs is proposed which allows objects and/or attribute parameters to be different across latent rater classes, and which allows to model dependencies between associations with a common object (attribute) by assuming that the link between features and objects (attributes) is fixed across judgements. Formal relationships with existing multilevel latent class models for binary three-way data are described. As an illustration, the models are used to study rater differences in product perception and to investigate individual differences in the situational determinants of anger-related behavior.     

Classification and Categorical Inputs with Treed Gaussian Process Models

Recognizing the successes of treed Gaussian process (TGP) models as an interpretable and thrifty model for nonparametric regression, we seek to extend the model to classification. Both treed models and Gaussian processes (GPs) have, separately, enjoyed great success in application to classification problems. An example of the former is Bayesian CART. In the latter, real-valued GP output may be utilized for classification via latent variables, which provide classification rules by means of a softmax function. We formulate a Bayesian model averaging scheme to combine these two models and describe a Monte Carlo method for sampling from the full posterior distribution with joint proposals for the tree topology and the GP parameters corresponding to latent variables at the leaves. We concentrate on efficient sampling of the latent variables, which is important to obtain good mixing in the expanded parameter space. The tree structure is particularly helpful for this task and also for developing an efficient scheme for handling categorical predictors, which commonly arise in classification problems. Our proposed classification TGP (CTGP) methodology is illustrated on a collection of synthetic and real data sets. We assess performance relative to existing methods and thereby show how CTGP is highly flexible, offers tractable inference, produces rules that are easy to interpret, and performs well out of sample.     

Latent class DEDICOM

A probabilistic DEDICOM model was proposed for mobility tables. The model attempts to explain observed transition probabilities by a latent mobility table and a set of transition probabilities from latent classes to observed classes. The model captures asymmetry in observed mobility tables by asymmetric latent mobility tables. It may be viewed as a special case of both the latent class model and DEDICOM with special constraints. A maximum penalized likelihood (MPL) method was developed for parameter estimation. The EM algorithm was adapted for the MPL estimation. Two examples were given to illustrate the proposed method. The work reported in this paper has been supported by grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada and by a fellowship of the Royal Netherlands Academy of Arts and Sciences to the second author. We would like to thank anonymous reviewers for their insightful comments.     

电子技术术语翻译技巧

术语是凝结一个学科系统知识的关键词。正确翻译术语对于准确理解科技文献和应用相关的科学技术至关重要。本文针对电子技术领域，通过一些电子技术术语示例，介绍了翻译电子技术术语的一些技巧。     

术语的认知整合

在术语的知识导向型研究中,学者围绕不同专业领域的术语知识表征先后推出系列概念模型。然而,此类研究并未明确兼顾术语的已有定义和实际使用语境,也未充分探索概念框架以外与术语相关的认知结构与建构。新近提出的认知整合模型是对这两点不足的尝试补充,在框架语义学与认知语言学基础上,改良了事件域认知模型,引入了概念隐喻、概念转喻、概念整合认知机制,兼顾术语的已有专业释义和实际使用语境进行研究。将其应用于探析中国绿茶定名,结果表明该模型能有效充实术语的知识表征与建构,有助于术语的理解与使用。     

论商务翻译教学中的术语翻译问题

文章从术语学角度出发讨论商务翻译教学中的术语翻译问题;介绍了术语的相关知识;探讨了商务翻译中的术语翻译问题;提出在商务翻译教学中要将术语的语符转换技巧和术语所表达的概念以及术语所包含的专业知识一起融合到商务翻译教学中,以提高商务翻译的教学水平和提升学生的商务翻译能力。     

基于多模态术语知识库的译者知识获取

掌握翻译实践所涉及的专业领域知识以及获取相关知识的方法,是译者应具备的职业素养,也是提升翻译服务质量的必然要求。多模态术语知识库整合语料库、术语库、关系库等语言知识素材,建立面向译者需求的数据分析机制与知识获取机制,并以可视化的人机交互手段优化知识的表示与利用环节,降低专业领域的知识壁垒。基于此,译者能够从中高效地获取和习得翻译过程中不可或缺的语言类与专业类知识,包括术语知识、搭配知识、概念实体知识与逻辑关系知识。     

A Thurstonian Ranking Model with Rank-Induced Dependencies

A Thurstonian model for ranks is introduced in which rank-induced dependencies are specified through correlation coefficients among ranked objects that are determined by a vector of rank-induced parameters. The ranking model can be expressed in terms of univariate normal distribution functions, thus simplifying a previously computationally intensive problem. A theorem is proven that shows that the specification given in the paper for the dependencies is the only way that this simplification can be achieved under the process assumptions of the model. The model depends on certain conditional probabilities that arise from item orders considered by subjects as they make ranking decisions. Examples involving a complete set of ranks and a set with missing values are used to illustrate recovery of the objects’ scale values and the rank dependency parameters. Application of the model to ranks for gift items presented singly or as composite items is also discussed.