標題: Titlebook: Bayesian Networks in Educational Assessment; Russell G. Almond,Robert J. Mislevy,David M. Willi Textbook 2015 Springer Science+Business Me [打印本頁] 作者: exterminate 時間: 2025-3-21 18:44
書目名稱Bayesian Networks in Educational Assessment影響因子(影響力)
書目名稱Bayesian Networks in Educational Assessment影響因子(影響力)學科排名
書目名稱Bayesian Networks in Educational Assessment網(wǎng)絡(luò)公開度
書目名稱Bayesian Networks in Educational Assessment網(wǎng)絡(luò)公開度學科排名
書目名稱Bayesian Networks in Educational Assessment被引頻次
書目名稱Bayesian Networks in Educational Assessment被引頻次學科排名
書目名稱Bayesian Networks in Educational Assessment年度引用
書目名稱Bayesian Networks in Educational Assessment年度引用學科排名
書目名稱Bayesian Networks in Educational Assessment讀者反饋
書目名稱Bayesian Networks in Educational Assessment讀者反饋學科排名
作者: 馬具 時間: 2025-3-21 22:24
IntroductionThis book explores the implications of applying Bayesian networks to educational assessment. This approach supports complex models as needed in for diagnostic testing and constructed response or interactive tasks, but it is also compatible with the models and techniques that have developed in psychometrics over the past century.作者: Accord 時間: 2025-3-22 03:58
Textbook 2015l sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments..Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm.作者: Antagonist 時間: 2025-3-22 06:43
Bayesian Probability and Statistics: a Reviewal independence, which will form the basic building blocks of our models. Graphical representation of probability models. Random variables. Bayes‘ theorem as a paradigm for learning about unknown quantities.作者: 慢慢流出 時間: 2025-3-22 11:37
Basic Graph Theory and Graphical Modelsce of an assessment area will rarely be comfortable with mathematical notation for expressing their ideas. To work with them, the psychometrician needs a representation which is rigorous, but intuitive enough for the substantive experts to be comfortable.作者: 鴿子 時間: 2025-3-22 15:28
Parameters for Bayesian Network Modelss that add a layer of probabilistic noise to logical functions such as AND and OR gates, producing the kinds of link functions seen in cognitive diagnosis. Second are models that use functions from normal regression theory and item response theory (such as Samejima‘s graded response model) to model probability tables more parsimoniously.作者: 轉(zhuǎn)換 時間: 2025-3-22 17:15 作者: 共和國 時間: 2025-3-22 23:15 作者: 夾死提手勢 時間: 2025-3-23 03:53 作者: 坦白 時間: 2025-3-23 07:33
Luca Bartocci,Damiana Lucentinie mathematical models can be refined with data. Although throughout the book there are references to cognitive processes that the probability distributions model, the full discussion of assessment design follows the discussion of the more mathematical issues.作者: 遠地點 時間: 2025-3-23 12:38 作者: Bmd955 時間: 2025-3-23 17:01
Advances in Intelligent and Soft Computinging educational measurement models in these terms. It then describes and illustrates two estimation approaches: Bayes modal estimation via the expectation–maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) estimation.作者: palette 時間: 2025-3-23 18:49 作者: 真實的你 時間: 2025-3-23 22:13
Sourav Mandal,Sudip Kumar NaskarThe chapter begins discussing the design process and the critical ECD concept of claims. The next sections discuss the six models of the CAF: proficiency models, task models, evidence models, assembly models, presentation models, and delivery models. The closing section discusses how these models work together to form the complete assessment.作者: 啤酒 時間: 2025-3-24 04:04
Anindita Mukherjee,Oshmita Dey,B. C. Giriss architecture describes agents (people, computers, or some mix) that actually carry out the operations—from determining what to to, to interacting with examinees, to capturing and evaluating theirwork, to creating and reporting results.作者: 愛好 時間: 2025-3-24 08:17 作者: 氣候 時間: 2025-3-24 12:08
Efficient Calculationshe variables on our beliefs about the remaining variables (i.e., to propagate the evidence). This chapter addresses efficient calculation in networks of discrete variables. The objective is to ground intuition with a simplified version of a basic junction-tree algorithm, illustrated in detail with a numerical example.作者: 反復(fù)拉緊 時間: 2025-3-24 18:29
Learning in Models with Fixed Structureing educational measurement models in these terms. It then describes and illustrates two estimation approaches: Bayes modal estimation via the expectation–maximization (EM) algorithm and Markov Chain Monte Carlo (MCMC) estimation.作者: Tortuous 時間: 2025-3-24 21:01 作者: jarring 時間: 2025-3-25 00:25
The Conceptual Assessment FrameworkThe chapter begins discussing the design process and the critical ECD concept of claims. The next sections discuss the six models of the CAF: proficiency models, task models, evidence models, assembly models, presentation models, and delivery models. The closing section discusses how these models work together to form the complete assessment.作者: 征稅 時間: 2025-3-25 07:20 作者: 政府 時間: 2025-3-25 09:05
https://doi.org/10.1007/978-3-319-74980-8al independence, which will form the basic building blocks of our models. Graphical representation of probability models. Random variables. Bayes‘ theorem as a paradigm for learning about unknown quantities.作者: echnic 時間: 2025-3-25 12:31 作者: pessimism 時間: 2025-3-25 17:16
Yubin Ji,Zhongyuan Qu,Xiang Zou,Chenfeng Jis that add a layer of probabilistic noise to logical functions such as AND and OR gates, producing the kinds of link functions seen in cognitive diagnosis. Second are models that use functions from normal regression theory and item response theory (such as Samejima‘s graded response model) to model probability tables more parsimoniously.作者: 說明 時間: 2025-3-25 22:13
Keith Grint,Peter Case,Leslie WillcocksFramework. It describes describes the four-process delivery system used in the prototype. The following chapter provides numerical details about the models and our efforts to refine the models from data.作者: Flounder 時間: 2025-3-26 01:19 作者: Landlocked 時間: 2025-3-26 04:50 作者: 表皮 時間: 2025-3-26 12:24 作者: Finasteride 時間: 2025-3-26 14:02
Russell G. Almond,Robert J. Mislevy,David M. WilliFeatures exercises to make the material concrete.ECD portions of the book (Ch. 2, 12 & 13) build on work that was basis for the 2000 NCME award for Outstanding Technical Contribution to Educational Me作者: abysmal 時間: 2025-3-26 18:50
Statistics for Social and Behavioral Scienceshttp://image.papertrans.cn/b/image/181868.jpg作者: Lumbar-Stenosis 時間: 2025-3-26 22:17 作者: 同來核對 時間: 2025-3-27 04:24
Explanation and Test Construction basis of those estimates. The quantity defined as the weight of evidence associated with a task is useful for explanation, as well as debugging models (explaining unexpected results). Expected weight of evidence is useful for assembling assessments either adaptively or in fixed forms.作者: 我們的面粉 時間: 2025-3-27 05:52
An Illustrative Examplehe network, learning the parameters of Bayes nets given a body of assessment data, and evaluating how well a proposed network fits the data. This chapter reviews these concepts in terms of an example: the mixed number subtraction example of Tatsuoka (1983).作者: MELD 時間: 2025-3-27 13:29 作者: Texture 時間: 2025-3-27 13:42
The Biomass Measurement ModelFrom one perspective, evidence-centered assessment design and Bayesian networks are just notations. In particular, it is easy to express familiar assessment design patterns using these notations. Bayesian networks are just a way to parameterize multidimensional latent class models. What have we gained?作者: 突變 時間: 2025-3-27 21:06 作者: 預(yù)測 時間: 2025-3-27 23:37
Mariusz Duplaga,Krzysztof Zielińskis used in discrete Bayes nets applications for assessment. It describes how, with the help of Bayes net software, to build and use Bayesian networks as the scoring engine for an assessment. It also illustrates some simple design patterns for building conditional probability tables.作者: flaunt 時間: 2025-3-28 03:14 作者: escalate 時間: 2025-3-28 07:55
Bidesh Chakraborty,Mamata Daluihe network, learning the parameters of Bayes nets given a body of assessment data, and evaluating how well a proposed network fits the data. This chapter reviews these concepts in terms of an example: the mixed number subtraction example of Tatsuoka (1983).作者: 轉(zhuǎn)向 時間: 2025-3-28 12:06 作者: Paraplegia 時間: 2025-3-28 17:37
978-1-4939-3828-5Springer Science+Business Media New York 2015作者: Urea508 時間: 2025-3-28 21:12
https://doi.org/10.1007/978-3-319-74980-8raph whose nodes represent the variables and whose edges represent dependencies between them, provides a guide for both constructing and computing with the statistical models. Discrete Bayesian networks, graphical models in which all the variables are discrete and all the edges are directed, have pa作者: Liberate 時間: 2025-3-29 00:31 作者: 難解 時間: 2025-3-29 03:15
https://doi.org/10.1007/978-3-319-74980-8 reviews the ideas, models, and concepts of probability and Bayesian inference that will be needed. The sections address the following topics: The basic definition of probability and its use in representing states of information. Conditional probability and Bayes‘ theorem. Independence and condition作者: 多產(chǎn)子 時間: 2025-3-29 08:25 作者: Fecal-Impaction 時間: 2025-3-29 11:42 作者: Pastry 時間: 2025-3-29 17:27
Mariusz Duplaga,Krzysztof Zielińskis used in discrete Bayes nets applications for assessment. It describes how, with the help of Bayes net software, to build and use Bayesian networks as the scoring engine for an assessment. It also illustrates some simple design patterns for building conditional probability tables.作者: 有組織 時間: 2025-3-29 22:44 作者: 愚蠢人 時間: 2025-3-30 02:40
Yubin Ji,Zhongyuan Qu,Xiang Zou,Chenfeng Ji distribution for the discrete Bayesian network. However, the hyper-Dirichlet has many parameters as table size increases, and it is often difficult to assess hyper-Dirichlet priors. The chapter thus explores two different approaches to reducing the number of parameters in the model. First are model作者: Indicative 時間: 2025-3-30 04:34 作者: ALLEY 時間: 2025-3-30 10:13 作者: Obligatory 時間: 2025-3-30 13:29
Bidesh Chakraborty,Mamata Daluihe network, learning the parameters of Bayes nets given a body of assessment data, and evaluating how well a proposed network fits the data. This chapter reviews these concepts in terms of an example: the mixed number subtraction example of Tatsuoka (1983).作者: 虛假 時間: 2025-3-30 18:44
Sourav Mandal,Sudip Kumar Naskart is the result of a design process beginning with an examination of the claims an assessment needs to ground and the evidence it needs to back them. The chapter begins discussing the design process and the critical ECD concept of claims. The next sections discuss the six models of the CAF: proficie作者: 半圓鑿 時間: 2025-3-31 00:44
