LEADER 05222nam 22006975 450 001 9910299770803321 005 20200707031824.0 010 $a1-4939-2125-8 024 7 $a10.1007/978-1-4939-2125-6 035 $a(CKB)3710000000379535 035 $a(SSID)ssj0001465375 035 $a(PQKBManifestationID)11896954 035 $a(PQKBTitleCode)TC0001465375 035 $a(PQKBWorkID)11470770 035 $a(PQKB)11461538 035 $a(DE-He213)978-1-4939-2125-6 035 $a(MiAaPQ)EBC6314345 035 $a(MiAaPQ)EBC5596284 035 $a(Au-PeEL)EBL5596284 035 $a(OCoLC)905223301 035 $a(PPN)184895723 035 $a(EXLCZ)993710000000379535 100 $a20150310d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian Networks in Educational Assessment /$fby Russell G. Almond, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, David M. Williamson 205 $a1st ed. 2015. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2015. 215 $a1 online resource (XXXIII, 662 p. 155 illus., 87 illus. in color.) 225 1 $aStatistics for Social and Behavioral Sciences,$x2199-7357 300 $aBibliographic Level Mode of Issuance: Monograph 320 $aIncludes bibliographical references (pages 607-638) and indexes. 327 $aIntroduction -- 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. 330 $aBayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets? foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume?s grounding in Evidence-Centered Design (ECD) framework for assessment design. This ?design forward? approach enables designers to take full advantage of Bayes nets? modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources. 410 0$aStatistics for Social and Behavioral Sciences,$x2199-7357 606 $aStatistics  606 $aArtificial intelligence 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aStatistics . 615 0$aArtificial intelligence. 615 14$aStatistics for Social Sciences, Humanities, Law. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aArtificial Intelligence. 676 $a371.2601519542 700 $aAlmond$b Russell G$4aut$4http://id.loc.gov/vocabulary/relators/aut$0117743 702 $aMislevy$b Robert J$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSteinberg$b Linda S$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYan$b Duanli$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWilliamson$b David M$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299770803321 996 $aBayesian Networks in Educational Assessment$92503199 997 $aUNINA