认知策略转换行为的心理计量学研究

传统的测量模型有一重要假设,即被试在完成测验过程中自始至终采用同一种策略.事实上,被试会根据题目类型不同而改变其解题策略,称之为策略转换.使用策略转换模型和潜在类别分析两种方法对平衡秤任务测验作答过程中的策略转换现象进行了分析比较.结果显示:策略转换模型存在策略位置参数越界、顺序混乱等缺陷,不适于策略转换问题研究;使用潜在类别分析方法可有效分析被试的策略转换行为,儿童在完成平衡秤任务测验时呈现出不同的策略转换路径.     

认知评价视域内的大学生应对类型

结合认知评价理论,划分大学生应对类型,探讨应对类型与心理健康的关系。使用应对方式问卷(CSQ)和症状自评量表(SCL-90)对西南地区1372名大学生施测。潜在类别模型显示大学生应对类型可划分为被动应对类型和主动应对类型,分别占比37. 3%和62. 7%;被动应对类型在CSQ各维度的组内相关比主动应对类型要大,且两者在各应对方式和心理健康总分上存在显著差异;结论:被动应对类型在选择应对策略时更盲目,不排除使用不成熟应对方式;主动应对类型能积极地选择应对策略,偏好问题解决应对方式。主动应对类型者的心理健康水平更高。     

教育考试中的认知诊断评估

认知诊断评估是教育和心理测量学界新兴起的一项技术,主要是在传统考试基础上,为学生提供诊断信息,即提供关于学生对知识/技能掌握情况的信息,对教育教学具有重要的实际价值。本文以认知诊断评估为研究对象,着重分析开展认知诊断评估工作的基本流程,以期为我国教育工作开展认知诊断评估提供参考。     

初中生二次函数认知诊断研究

基于DINA认知诊断模型,对初中生二次函数的认知情况进行诊断研究.先确定二次函数认知属性及属性层级关系,再根据Q矩阵编制二次函数认知诊断测试卷,最后对认知属性掌握概率和掌握模式进行分析.     

基于认知诊断的儿童空间认知能力研究

空间认知能力是儿童智力发展的重要组成部分,对儿童的发展起着重要作用。基于此,对空间认知能力进行了研究,首先阐述了空间认知能力的概念,其次分析了空间认知能力的属性,最后基于属性多级化模型的儿童空间认知能力进行了实例分析,以期促进学生核心素养的发展。     

分类准确性系数在高中化学热化学认知诊断测验中的应用

本研究以高中化学热化学领域为例,考察在认知诊断测验中,最新提出的Cui和Wang分类准确性系数的应用。选取DINA模型,分析了694名高二考生的知识掌握状态及分类准确性系数。同时,抽样选取30名考生的试卷,由教师诊断给出考生真实知识掌握状态,与模型对比得到实证分类准确性系数。结果表明:(1)模型估计的分类准确性系数和实证分类准确性系数较为吻合,可在实际测验中应用;(2)教师可以在模型估计结果的基础上,结合自身判断进行教学指导。     

特殊教育中的认知诊断研究

认知诊断主要将认知心理学与现代教育测量方法相结合,通过获得受试个体在测试中的作答反应来推测其不可观察的知识状态,以测量该个体特定的知识结构和加工技能。本文将研究利用建立 Q矩阵对特殊人群的学力结构进行认知诊断的方法,从而实现因材施教,实现个性化教育。     

儿童在平衡秤任务上的认知规则及评估方法

平衡秤任务是儿童高级认知策略研究非常著名的一种形式,Siegler提出的规则评估技术极大地推动了解题规则的相关研究,但在应用过程中显现了规则评估技术的不稳定性等诸多局限。目前,平衡秤任务的解题规则的研究方法发展迅速,基于潜变量的分析方法已克服了这些不足,在统计方面具有明显优势。因此,我国教育与心理研究者应关注使用潜在类别分析等方法解决高级认知策略研究中的各种问题。     

潜在类别模型的原理、步骤及程序

潜在类别分析和潜在剖面分析相应的模型统称为潜在类别模型,它是根据个体在观测指标上的不同反应模式将其进行分类,从而达到识别群体异质性的一类统计方法,在教育学、心理学等社科领域日益受到应用研究者的关注。然而现有的中文文献对此类模型的统计原理和分析步骤的介绍不易为教育研究工作者所接受。本文系统讲述潜在类别模型的基础知识、统计原理、分析步骤和Mplus程序,厘清潜在类别模型的后续分析所涉及的多种方法和选用策略,有助于增进应用研究者对潜在类别模型的原理理解和方法掌握,推动潜在类别模型在教育研究中的应用。     

基于DINA模型的高中离子反应认知诊断研究

基于DINA认知诊断模型,对高一化学离子反应进行认知诊断研究.研究结果表明:离子反应7个认知属性和属性间层级关系是合理的,基于Q矩阵理论编制的离子反应认知诊断试卷诊断效果较好,学生的认知属性掌握模式和掌握概率有差异,掌握模式比分数更能诊断学生的认知情况.基于此,对当前高一化学教学提出了合理建议.     

Multilevel Cognitive Diagnosis Models for Assessing Changes in Latent Attributes

Cognitive diagnosis models (CDMs) have been developed to evaluate the mastery status of individuals with respect to a set of defined attributes or skills that are measured through testing. When individuals are repeatedly administered a cognitive diagnosis test, a new class of multilevel CDMs is required to assess the changes in their attributes and simultaneously estimate the model parameters from the different measurements. In this study, the most general CDM of the generalized deterministic input, noisy “and” gate (G‐DINA) model was extended to a multilevel higher order CDM by embedding a multilevel structure into higher order latent traits. A series of simulations based on diverse factors was conducted to assess the quality of the parameter estimation. The results demonstrate that the model parameters can be recovered fairly well and attribute mastery can be precisely estimated if the sample size is large and the test is sufficiently long. The range of the location parameters had opposing effects on the recovery of the item and person parameters. Ignoring the multilevel structure in the data by fitting a single‐level G‐DINA model decreased the attribute classification accuracy and the precision of latent trait estimation. The number of measurement occasions had a substantial impact on latent trait estimation. Satisfactory model and person parameter recoveries could be achieved even when assumptions of the measurement invariance of the model parameters over time were violated. A longitudinal basic ability assessment is outlined to demonstrate the application of the new models.     

Skills Diagnosis Using IRT-Based Continuous Latent Trait Models

This article summarizes the continuous latent trait IRT approach to skills diagnosis as particularized by a representative variety of continuous latent trait models using item response functions (IRFs). First, several basic IRT-based continuous latent trait approaches are presented in some detail. Then a brief summary of estimation, model checking, and assessment scoring aspects are discussed. Finally, the University of California at Berkeley multidimensional Rasch-model-grounded SEPUP middle school science-focused embedded assessment project is briefly described as one significant illustrative application.     

心理测量理论的新进展：潜在分类模型

随着心理测量理论的发展,潜在分类模型越来越多地受到国外研究者的关注,它能够通过分析考生的作答过程来探讨其潜在能力倾向,改变了以总分来评判学生能力的传统评价模式。首先探讨了作为该类模型基础的规则空间模型,然后综述了近年来国外有关潜在分类模型的研究,最后对该模型的研究现状进行了评价并对未来发展进行了展望。     

心理测量理论的新进展:潜在分类模型

认知诊断模型是新一代心理测量理论——认知诊断理论的核心。它可分为潜在特质模型和潜在分类模型两大类。其中,潜在分类模型主要用于分析被试的作答过程从而探讨被试的潜在知识结构,克服了CCT和IRT的缺陷,开创了教育与心理测量领域新的里程碑。本文首先介绍作为该类模型基础的规则空间模型,然后集中探讨在此基础上发展起来的较新的潜在分类模型,最后对这类模型进行了评价和展望。     

Bayesian Analysis of Multivariate Latent Curve Models With Nonlinear Longitudinal Latent Effects

In longitudinal studies, investigators often measure multiple variables at multiple time points and are interested in investigating individual differences in patterns of change on those variables. Furthermore, in behavioral, social, psychological, and medical research, investigators often deal with latent variables that cannot be observed directly and should be measured by 2 or more manifest variables. Longitudinal latent variables occur when the corresponding manifest variables are measured at multiple time points. Our primary interests are in studying the dynamic change of longitudinal latent variables and exploring the possible interactive effect among the latent variables.

Much of the existing research in longitudinal studies focuses on studying change in a single observed variable at different time points. In this article, we propose a novel latent curve model (LCM) for studying the dynamic change of multivariate manifest and latent variables and their linear and interaction relationships. The proposed LCM has the following useful features: First, it can handle multivariate variables for exploring the dynamic change of their relationships, whereas conventional LCMs usually consider change in a univariate variable. Second, it accommodates both first- and second-order latent variables and their interactions to explore how changes in latent attributes interact to produce a joint effect on the growth of an outcome variable. Third, it accommodates both continuous and ordered categorical data, and missing data.     

真实情境中地理诊断问题解决的认知模式研究

地理问题解决能力的水平分层是地理核心素养测量的实质基础。本研究在已有研究的基础上,以真实地理情境为研究任务,收集不同教育水平被试解决地理问题的过程信息,采用质性研究方法,探索地理问题解决的不同认知模式。研究发现,真实情境中的地理问题解决认知模式存在三种类型,它们在问题解决步骤、问题分类、问题表征、认知策略等方面存在不同程度的共性和差异。本研究的发现,为进一步研究中学生在真实地理情境中问题解决的学习进阶,构建地理核心素养背景下的地理学业评价框架提供实证研究基础。     

具有变化潜伏期的水源性疾病模型稳定性分析

建立了一类具有变化潜伏期的水源性疾病数学模型,得到了水源性疾病流行的阈值R0（基本再生数）.利用LaSalle不变集原理,通过构造新的Liapunov函数证明了平衡点的全局稳定性：当R0≤1时,系统的无病平衡点p0是全局渐近稳定的;当R0＞1时,系统的地方病平衡点p＊是全局渐近稳定的.最后利用数值模拟说明结论的正确性.     

MIMIC Latent Class Analysis Model for Alcoholic Diagnosis

The multiple indicators multiple causes (MIMIC) latent class analysis (LCA) model is an excellent classification method when researchers cannot find a "gold standard" to classify participants. The MIMIC-LCA model includes features of a typical LCA model and also introduces a new relation between the latent class and covariates. In other words, a logistic regression type of analysis between participants' categorical latent status and their background information is added. Detailed statistical setups of the MIMIC-LCA model and algorithmic procedures are derived. The model features, parameter estimations, and model selections for MIMIC-LCA models are also presented. Specifically, the MIMIC-LCA model is estimated by a generalized expectation-maximization algorithm under the maximum likelihood frameworks. A substantive application of the MIMIC-LCA model in diagnosing alcoholics and, in particular, examining potential risk factors for alcoholism is demonstrated.