英语听力在线测试的探索与应用

英语听力在线测试系统是一种以发展理论为指导的形成性评估形式。通过量化与质化相结合的研究方法分析了该在线测试系统,包括自动批卷、成绩反馈和随机出题等功能的实施效果。证明了英语听力在线测试系统在促进英语听力学习良性发展中的潜力与推动作用。     

新版布鲁姆教育目标分类对外语教学与测试改革的启示

本文简要概述了对教学与测试的发展曾产生重大影响的旧版布鲁姆认知领域教育目标分类体系,着重介绍国内论述较少的新版分类理念和内容,评述新旧版本的异同,以及新版的应用实例,以期对国内教育界尤其是外语教学与测试的改革提供参考.     

浅析英语测试手段的现代化变革

随着多媒体技术的广泛应用,英语测试正从个性化、互动网络化、公正客观化等六方面呈现现代化发展趋势。广大英语教师应该充分利用现代化测试手段带来的优势和便利,积极研究探索高效的阅卷评分方式,开发相应的测试结果数据分析系统,努力实现英语测试的高信度和高效度,推动大学英语教学改革。     

基于动态规划的测试用例的优化分配问题

通过对划分测试中测试资源受约束、各子域满足一定可靠性要求时测试用例的优化分配问题研究,把非线性整数规划问题转化为无界背包问题,用动态规划方法对它求解,通过随机模拟,给出了最优分配方案。实验数据表明,该方案在测试过程中对提高缺陷检测能力有明显的效果。     

Bayesian decision theoretic approach to hypothesis problems with skewed alternatives

Many hypothesis problems in practice require the selection of the left side or the right side alternative when the null is rejected. For parametric models, this problem can be stated as _H0:θ=_θ0$H_0:\theta ={\theta }_0$vs.  _H−:θ<_θ0$H_{-}:\theta <{\theta }_0$ or _H+:θ>_θ0$H_{+}:\theta >{\theta }_0$. Frequentists use Type-III error (directional error) to develop statistical methodologies. This approach and other approaches considered in the literature do not take into account the situations where the selection of one side may be more important or when one side may be more probable than the other. This problem can be tackled by specifying a loss function and/or by specifying a hierarchical prior structure with allowing the skewness in the alternatives. Based on this, we develop a Bayesian decision theoretic methodology and show that the resulted Bayes rule perform better in the side of the alternatives which is more probable. The methodology can be also used in a frequentist's framework when it is desired to discover an alternative that is more important. We also consider the multiple hypotheses problem and develop new false discovery rates for the selection of the left and the right sides of alternatives. These discovery rates would be useful in the situations when one side of the alternatives are more important or more probable than the other.     

A note on estimating the false discovery rate under mixture model

In this note, we focus on estimating the false discovery rate (FDR) of a multiple testing method with a common, non-random rejection threshold under a mixture model. We develop a new class of estimates of the FDR and prove that it is less conservatively biased than what is traditionally used. Numerical evidence is presented to show that the mean squared error (MSE) is also often smaller for the present class of estimates, especially in small-scale multiple testings. A similar class of estimates of the positive false discovery rate (pFDR) less conservatively biased than what is usually used is then proposed. When modified using our estimate of the pFDR and applied to a gene-expression data, Storey's q-value method identifies a few more significant genes than his original q-value method at certain thresholds. The BH like method developed by thresholding our estimate of the FDR is shown to control the FDR in situations where the p  -values have the same dependence structure as required by the BH method and, for lack of information about the proportion _π0${\pi }_0$ of true null hypotheses, it is reasonable to assume that _π0${\pi }_0$ is uniformly distributed over (0,1).     

Selecting the best geometric distribution based on type-I censored data: a Bayesian approach

This paper considers the statistical reliability on discrete failure data and the selection of the best geometric distribution having the smallest failure probability from among several competitors. Using the Bayesian approach a Bayes selection rule based on type-I censored data is derived and its associated monotonicity is also obtained. An early selection rule which allows us to make a selection possible earlier than the censoring time of the life testing experiment is proposed. This early selection rule can be shown to be equivalent to the Bayes selection rule. An illustrative example is given to demonstrate the use and the performance of the early selection rule.     

Projection‐Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments

It is well known that standard asymptotic theory is not applicable or is very unreliable in models with identification problems or weak instruments. One possible way out consists of using a variant of the Anderson–Rubin ((1949), AR) procedure. The latter allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, but not for individual coefficients. This problem may in principle be overcome by using projection methods (Dufour (1997), Dufour and Jasiak (2001)). At first sight, however, this technique requires the application of costly numerical algorithms. In this paper, we give a general necessary and sufficient condition that allows one to check whether an AR‐type confidence set is bounded. Furthermore, we provide an analytic solution to the problem of building projection‐based confidence sets from AR‐type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are needed to build the confidence intervals.     

关于大学英语考试的效度与效应

本文在大量数据的基础上分析了大学英语考试效度、反拨效应等要素 ,论证了大学英语考试对大学英语的积极的促进作用 ;并针对考试客观上产生的负面影响 ,提出自己的建议。