Developing Score Reports for Cognitive Diagnostic Assessments

This paper presents a framework to provide a structured approach for developing score reports for cognitive diagnostic assessments (CDAs). Guidelines for reporting and presenting diagnostic scores are based on a review of current educational test score reporting practices and literature from the area of information design. A sample diagnostic report is presented to illustrate application of the reporting framework in the context of one CDA procedure called the Attribute Hierarchy Method. Integration and application of interdisciplinary techniques from education, information design, and technology are required for effective score reporting. While the AHM is used in this paper, this framework is applicable to any attribute-based diagnostic testing method.     

教育认知诊断测验的信度和效度研究评述

信度和效度是衡量一个测量工具质量的关键指标,教育认知诊断测验中的信度和效度研究近年来受到研究者的关注。诊断测验的信度系数基本上源自基于α系数的属性信度系数、经验属性信度系数、四分相关系数、模拟重测一致性和分类一致性指标;效度系数主要包括模拟判准率、分类准确性和理论构想效度等。教育认知诊断测验的信度和效度研究较新,仍存在着一定的不足且缺乏全面的比较研究,更缺少系统的评价体系。     

Software Packages for Bayesian Multilevel Modeling

Multilevel modeling is a statistical approach to analyze hierarchical data that consist of individual observations nested within clusters. Bayesian method is a well-known, sometimes better, alternative of Maximum likelihood method for fitting multilevel models. Lack of user friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed.     

关于大学物理实验的教学改革

介绍了韶关学院实验中心在大学物理基础实验体系、实验内容及实验方法等方面进行的改革与探索．以及在初步建立新的实验教学体系方面的创新尝试。     

贝叶斯网络对故障树方法的改进

通过建立映射 ,引入作为目前处理概率知识基础上不确定性问题的最有力的推断方法———贝叶斯网络 (BN) ,有效地解决可靠性分析中最常用的故障树分析 (FTA)方法的局限性 :各部件独立 ,状态为二值等。     

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes and illustrates key features of Bayesian approaches to model diagnostics and assessing data–model fit of structural equation models, discussing their merits relative to traditional procedures.     

贝叶斯网络在电控发动机故障诊断中的应用

介绍了发动机故障自诊断系统的诊断原理,指出了发动机自诊断系统已经不能满足电控汽油喷射系统故障诊断的要求,在此基础上介绍了贝叶斯网络的故障诊断方法.借助故障模拟试验数据确定了发动机怠速不良时电控汽油喷射系统贝叶斯网络故障诊断中各节点的先验概率值,然后通过贝叶斯网络找出了发动机怠速不良时电控汽油喷射系统各部件的故障发生概率.     

一种传感器网络目标定位的建模方法

本文提出一种传感器网络目标定位系统的系统模型,然后利用PtolemyⅡ建模仿真平台完成该传感器网络目标定位系统的系统级行为描述,并根据系统级建模方法学对该目标定位系统进行建模与仿真,分析了传感器网络目标定位系统的诸多影响因素.测试结果表明:传感器网络目标定位系统对于传感器节点的功耗及信息时间同步窗口具有敏感性,符合传感器网络目标定位系统应用特性,验证了Ptolemy嵌入式系统级设计方法学对于指导传感器网络系统设计的正确性和可行性.     

A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling

Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks.     

Modeling and Defending Passive Worms over Unstructured Peer-to-Peer Networks

Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.     

基于RBF神经网络的ZGMn13堆焊焊条的设计

提出了一种基于RBF神经网络的CaO—CaF2-SiO2渣系ZGMn13堆焊焊条配方优化设计方法．利用实验采集的数据对网络进行训练,以加工硬化后的硬度为优化目标,得到最优的焊条配方．实验结果表明：优化后熔敷金属的动载加工硬化性能和静载加工硬化性能良好。     

Artificial Neural Networks for Modeling Knowing and Learning in Science

Recent neurobiological evidence suggests that environmentally derived activity plays a central role in regulating neuronal growth and neuronal connectivity. Artificial neural networks with distributed representations display many features of knowing and learning that are known from biological intelligence. In this article, I advocate artificial neural networks as models for cognition and development. These models and how they work are exemplified in the context of a well‐known Piagetian developmental task and school science activity: balance beam problems. I conclude that artificial neural networks, because of their profoundly interactivist nature, are ideal tools for modeling cognitive development and learning in science. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 63–80, 2000     

基于贝叶斯网络的电控发动机故障诊断研究

分析了贝叶斯网络的拓扑结构,给出了发动机怠速不良的故障树图．然后根据故障树到贝叶斯网络的转化算法,建立了贝叶斯网络模型．借助故障模拟试验数据确定了发动机怠速不良时电控汽油喷射系统贝叶斯网络故障诊断中各节点的先验概率值,然后通过贝叶斯网络找出了发动机怠速不良时电控汽油喷射系统各部件的故障发生概率．     

Attitudes toward Statistics Assessments

Although statistics instructors have attempted to improve the cognitive aspects of instruction, particularly with respect to assessments, relatively little attention has been paid to non-cognitive issues, including students' attitudes, feelings, beliefs, perceptions, motivations, and interests. Thus, the purpose of the present study was to determine (1) the methods of statistics assessments that students most prefer; (2) the methods of statistics assessment that students feel induce the least amount of anxiety; (3) the methods of statistics assessments that students most rate as inducing higher-order thinking; (4) the characteristics of students with the most negative overall attitudes toward statistics assessments; and (5) how students rate performance assessments and authentic assessments. Findings revealed that students appear most to prefer statistics examinations in which at least limited supporting material is permitted. Interestingly, assessments in which some form of supporting material is allowed appeared to be more popular than are examinations with no time constraints. However, examinations that are untimed and in which supporting material is allowed were regarded best as inducing the least amount of anxiety, as increasing levels of performance, and as promoting higher-order thinking. Overall, students tended to rate performance assessments the most highly. Unfortunately, authentic assessments were not rated as highly as were performance assessments. Canonical correlation analyses suggested that age, the number of college-level mathematics courses, the number of years elapsed since students' last statistics class, and levels of statistics anxiety are determinants of these attitudes. The implications of these findings are discussed, and recommendations for future research are presented.     

Generalizability of Performance Assessments

How can the contributions of raters and tasks to error variance be estimated? Which source of error variance is usually greater? Are interrater coefficients adequate estimates of reliability? What other facets contribute to unreliability in performance assessments?     

基于贝叶斯推断的多层软测量建模在丁醇发酵中的应用

针对丁醇生产过程中发酵产物品质参量难以实时测量,现有测量方法精度不高、测量结果受不确定因素影响较大的问题,提出一种基于贝叶斯推断和支持向量回归（Support vector machine regression,SVR）的多层软测量建模方法。首先应用贝叶斯推断计算后验概率、筛选偏置数据,并对偏置数据校准,建立第一层SVR模型;然后利用贝叶斯推断进行二次校准,建立第二层SVR模型,对第一层SVR模型输出进行修正,得到最终预测结果,克服干扰和偏差引起的模型不准确问题。将基于贝叶斯推断的多层支持向量回归（Bi-SVR）预测模型应用于丁醇发酵过程,仿真及实验结果表明,相较于传统SVR预测模型,系统在低干扰的情况下预测精度提高了4.52%,在高干扰时预测精度提高了5.37%。     

基于HJPS算法的神经网络的轧制力修正模型

传统的轧制力模型结构简单、精度较低,即使采用基于有限元的数值积分方式进行精化,出于计算效率的考虑因其有限区域的划分十分有限,因此对于轧制力计算的精度提高有限。直接采用神经网络对轧制力进行建模可以极大地提高模型精度,但是模型对新型材料的泛化能力较差。为此提出简单有限元轧制力模型,并在模型基础上使用HJPS优化算法的神经网络对轧制力进行修正,对该模型的仿真测试表明,该模型具有很强的泛化能力,收敛速度快、不易陷于局部优化,能够极大地提高轧制力模型的计算精度。