語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Bayesian networks in educational ass...
~
Almond, Russell G.
Bayesian networks in educational assessment
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Bayesian networks in educational assessmentby Russell G. Almond ... [et al.].
其他作者:
Almond, Russell G.
出版者:
New York, NY :Springer New York :2015.
面頁冊數:
xxxiii, 666 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer eBooks
標題:
Bayesian statistical decision theory.
電子資源:
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:
182005
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
462779
003
DE-He213
005
20151028164549.0
006
m d
007
cr nn 008maaau
008
151119s2015 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
182005
650
0
$a
Educational tests and measurements
$x
Statistical methods.
$3
470588
650
0
$a
Educational evaluation
$x
Statistical methods.
$3
593380
650
1 4
$a
Statistics.
$3
182057
650
2 4
$a
Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
$3
274394
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
348605
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
252959
700
1
$a
Almond, Russell G.
$3
715971
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Statistics for social and behavioral sciences.
$3
560039
856
4 0
$u
http://dx.doi.org/10.1007/978-1-4939-2125-6
950
$a
Mathematics and Statistics (Springer-11649)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000112482
電子館藏
1圖書
電子書
EB QA279.5 B357 2015
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-1-4939-2125-6
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入