试卷难度评估体系与控制模型

给出了试题难度和试卷难度的一种评估指标体系,讨论了试题、试卷难度的控制方法,并给出了试卷难度的一种控制模型和实现思路,以及该难度控制技术在智能型试卷自动生成系统中的实现,为智能型试卷自动生成系统中试卷难度的控制提供了理论依据.     

通过试题难度控制试卷难度方法的探讨

命题是考试工作中非常重要的一个环节。一份试卷中各个试题的难度以及各级难度试题所占的比例将最终决定一份试卷的难度。文章通过分析试题难度,对试题难度进行量化后,根据试卷中各级难度试题所占的比例计算整份试卷的难度。考试结束后根据实测试卷难度和预估的试卷难度进行比较,为以后的工作提供依据。     

一种计算试卷中试题难度分布的有效方法

该文提出一种依据正态分布函数计算试卷中试题难度分布的方法，并将茯运用于智能组卷系统，计算机仿真结构表明了该方法的有效性。     

智能组卷中一种控制试卷难度分布的有效方法

在智能组卷中试题难度分布设计是否合理至关重要。根据教育测量学和考试学的理论，考生的成绩应大致呈正态分布。我们可让试卷中各种难度的试题分数也大致呈正态分布，这样就可通过改变试卷的难度分配达到控制考试平均成绩的目的。     

遗传模拟退火智能组卷策略研究

智能组卷是一个包含多重约束条件的最优组合问题,设计有效算法求解优化问题对获得高满意度试卷至关重要,为此,提出了一种基于模拟退火与标准遗传算法的混合智能组卷算法。约束模型采用了基于期望平均分的难度分布函数计算试卷难度系数,该算法模型采用分组实数编码,单点交叉算子,单个和随机两种基因变异算子,并对每代个体进行模拟退火操作。给出了详细的实验对比结果,表明了该算法的有效性。     

基于Web的在线考试系统的设计与实现

针对目前传统考试效率低下的问题,该文设计并实现了一个在线考试系统,系统包括对系统中的试题库的详细结构设计,设计了抽题算法,通过采用随机抽取与设置难度系数相结合的方式使试卷中各种难度试题数量的分布更加合理、科学,系统使用方便,有效。     

基于区分度的智能组卷难度正态分布算法

给出了一种依据试卷区分度确定算法参数的智能组卷难度正态分布算法。实例表明,相对于目前的通过设定组卷难度区间的概率和为1来确定算法参数,生成的试卷更好地体现了组卷难度分布设计所要求的试卷对不同考生的知识能力的区分和鉴别的考核要求。     

基于模拟退火遗传算法的自动组卷方法研究

针对试卷生成方面的多条件约束问题,采用离散型随机变量的二项分布函数建立难度-分数对应关系模型。根据试卷的最优试题难度分布结构,构造了适应度函数,建立了产生最优试卷的优化模型。在计算过程中,对试卷试题区分度,答题时间,试卷总分先行判断,从而缩短对最优试卷的寻优时间,提高成卷效率。为了避免遗传算法早期容易早熟、后期收敛速度慢的缺点,对试卷个体采用模拟退火算法进行适应度的拉伸。理论分析和实验结果表明,提出的组卷算法有较好的性能,可以有效的提高组卷效率。     

基于区分度的智能组卷难度正态分布算法

给出了一种依据试卷区分度确定算法参数的智能组卷难度正态分布算法.实例表明,相对于目前的通过设定组卷难度区间的概率和为1来确定算法参数,生成的试卷更好地体现了组卷难度分布设计所要求的试卷对不同考生的知识能力的区分和鉴别的考核要求.     

试卷质量控制技术的研究

文中提出了编制试题的基本要求，分析了影响试卷质量的基本因素，讨论了控制试题质量和试卷质量的技术指标．给出了一种提高试卷质量的制卷方法，并用于智能型试卷自动生成系统，为智能型试卷自动生成系统中试卷质量控制提供了一种较为合理的有效方法。     

基于离散化误差补偿的试题难度题量控制方法

自动组卷是高校实现考试规范化、科学化的重要手段。考生的平均成绩可以通过试卷的平均难度来控制。然而,如何确定各种难度的题量是关键技术。利用正态分布来确定各种难度的题量,是目前研究的一个方向。提出了对难度进行离散化正态分布后,利用对称积分求取各种难度的概率分布,并且采用比例误差补偿方法减少误差。同时,根据自动组卷的具体问题,确定了标准差取0. 2更加合适,并且给出了在不同的总题量下各种难度题量的分配方案。结果表明,基于离散化误差补偿的试题难度题量控制方法能够较好地控制各种难度的题量,为自动组卷策略提供了有效的依据。     

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented.     

自适应考试难度的智能组卷过程的研究与实现

为了适应各类考试难度不断变化的需要,考试题库中试题的难度级别通常要服从正态分布并且各难度级别的试题被抽中的概率会随考试难度的变化而调整。本文采用离散定义域内正态分布数值法确定各难度级别要抽取的试题题量,进而采用分段分类法和线性乘同余方法均匀随机抽取各难度级别规定的试题题量来生成试卷。该方法具有流程简单、运行速度快、效率高、选题重点突出、适应试卷平均难度多变以及可行性好等特点,已经应用于会计综合考试平台中。     

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n^−1/2 and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications.     

基于PB的通用自动组卷系统开发

介绍了用PB 9.0开发通用自动组卷系统的方法.该系统可以根据用户提供的试卷题目类型、分值、章节分布、难易程度等参数,自动从题库中随机抽题组成一份符合要求的格式规范的试卷.系统可以将同一门课程连续2年之内的试卷重复率控制在25%以下,并且可以自动生成答题纸和答案,大大减轻了教师出卷的负担.     

Monte Carlo方法与计算机模拟应用研究

介绍了Monte Carlo方法,提出其在模拟Buffer问题时存在的一个问题,并给出改进的方法;提出了用Monte Carlo方法产生任意分布随机变量的原理及方法,并对Beta分布和标准正态分布随机变量进行了计算机模拟和效果检验。     

蒙特卡罗法用于多指标试验设计的优化计算

以一优化的问题为例,探讨应用蒙特卡罗法去计算多指标试验设计的优化问题。展示及解释了蒙特卡罗法的BASICI程序,比较蒙特卡罗法与网格法的优化结果,原来两种结果几乎相同。但实践表明,蒙特卡罗法既可用于多指标试验设计的优化计算,又方便灵活地实现多指标优化的目的。     

Portmanteau Model Diagnostics and Tests for Nonlinearity: A Comparative Monte Carlo Study of Two Alternative Methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes.     

ANOVA extensions for mixed discrete and continuous data

This paper is concerned with ANOVA-like tests in the context of mixed discrete and continuous data. The likelihood ratio approach is used to obtain a location test in the mixed data setting after specifying a general location model for the joint distribution of the mixed discrete and continuous variables. The approach allows the problem to be treated from a multivariate perspective to simultaneously test both the discrete and continuous parameters of the model, thus avoiding the problem of multiple significance testing. Moreover, associations among variables are accounted for, resulting in improved power performance of the test. Unlike existing distance-based alternatives which rely on asymptotic theory, the likelihood ratio test is exact. In addition, it can be viewed as an extension to the mixed data setting of the classical multivariate ANOVA. We compare its performance against those of currently available tests via Monte Carlo simulations. Two real-data examples are presented to illustrate the methodology.     

The estimation of probability distribution of SDE by only one sample trajectory

This paper focuses on the method of the simulation of a stochastic system and the main method of our paper is the Monte Carlo computation simulation method. Taking the stochastic Logistic equation as an example, we present the simulation of the sample trajectory by Euler scheme and the invariant probability distribution of stochastic differential equations with the Monte Carlo method. We also compare the simulation result with the analytical result for the autonomous stochastic Logistic model. Moreover, the stochastic Logistic equation with Markovian switching which is described by a Markov chain taking values in a finite state space is considered.