東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Bayesian networks in educational ass...

Almond, Russell G.

Linked to FindBook

Google Book

Amazon

博客來

Bayesian networks in educational assessment

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian networks in educational assessment/ by Russell G. Almond ... [et al.].
other author:	Almond, Russell G.
Published:	New York, NY :Springer New York : : 2015.,
Description:	xxxiii, 666 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- An Introduction to Evidence-Centered Design -- Bayesian Probability and Statistics: a review -- Basic graph theory and graphical models -- Efficient calculations -- Some Example Networks -- Explanation and Test Construction -- Parameters for Bayesian Network Models -- Learning in Models with Fixed Structure -- Critiquing and Learning Model Structure -- An Illustrative Example -- The Conceptual Assessment Framework -- The Evidence Accumulation Process -- The Biomass Measurement Model -- The Future of Bayesian Networks in Educational Assessment -- Bayesian Network Resources -- References.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theory. -
Online resource:	http://dx.doi.org/10.1007/978-1-4939-2125-6
ISBN:	9781493921256 (electronic bk.)

Bayesian networks in educational assessment
Bayesian networks in educational assessment[electronic resource] /by Russell G. Almond ... [et al.]. - New York, NY :Springer New York :2015. - xxxiii, 666 p. :ill. (some col.), digital ;24 cm. - Statistics for social and behavioral sciences,2199-7357. - Statistics for social and behavioral sciences..

Introduction -- An Introduction to Evidence-Centered Design -- Bayesian Probability and Statistics: a review -- Basic graph theory and graphical models -- Efficient calculations -- Some Example Networks -- Explanation and Test Construction -- Parameters for Bayesian Network Models -- Learning in Models with Fixed Structure -- Critiquing and Learning Model Structure -- An Illustrative Example -- The Conceptual Assessment Framework -- The Evidence Accumulation Process -- The Biomass Measurement Model -- The Future of Bayesian Networks in Educational Assessment -- Bayesian Network Resources -- References.

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social 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. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC) A unique feature is the volume's grounding in Evidence-Centered Design (ECD) framework for assessment design. This "design forward" approach enables designers to take full advantage of Bayes nets' modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

ISBN: 9781493921256 (electronic bk.)

Standard No.: 10.1007/978-1-4939-2125-6doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.5

Bayesian networks in educational assessment
LDR:03328nmm a2200325 a 4500 001 1998061
003 DE-He213
005 20151028164549.0
006 m d
007 cr nn 008maaau
008 151112s2015 nyu s 0 eng d
020 $a 9781493921256 (electronic bk.)
020 $a 9781493921249 (paper)
024 7 $a 10.1007/978-1-4939-2125-6 $2 doi
035 $a 978-1-4939-2125-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5
072 7 $a JHBC $2 bicssc
072 7 $a SOC027000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA279.5 $b .B357 2015
245 0 0 $a Bayesian networks in educational assessment $h [electronic resource] / $c by Russell G. Almond ... [et al.].
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2015.
300 $a xxxiii, 666 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Statistics for social and behavioral sciences, $x 2199-7357
505 0 $a Introduction -- An Introduction to Evidence-Centered Design -- Bayesian Probability and Statistics: a review -- Basic graph theory and graphical models -- Efficient calculations -- Some Example Networks -- Explanation and Test Construction -- Parameters for Bayesian Network Models -- Learning in Models with Fixed Structure -- Critiquing and Learning Model Structure -- An Illustrative Example -- The Conceptual Assessment Framework -- The Evidence Accumulation Process -- The Biomass Measurement Model -- The Future of Bayesian Networks in Educational Assessment -- Bayesian Network Resources -- References.
520 $a Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social 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. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC) A unique feature is the volume's grounding in Evidence-Centered Design (ECD) framework for assessment design. This "design forward" approach enables designers to take full advantage of Bayes nets' modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.
650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Educational tests and measurements $x Statistical methods. $3 1074078
650 0 $a Educational evaluation $x Statistical methods. $3 1624049
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 894294
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Almond, Russell G. $3 2139575
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Statistics for social and behavioral sciences. $3 1567990
856 4 0 $u http://dx.doi.org/10.1007/978-1-4939-2125-6
950 $a Mathematics and Statistics (Springer-11649)