000 | 05930nam a2200853 i 4500 | ||
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001 | a13962651 | ||
001 | ucon1154613526 | ||
003 | KOHA | ||
005 | 20220415140407.0 | ||
008 | 220412t2020 ncud 000 0 eng d | ||
020 |
_a9781641139519 _q(paperback) |
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020 |
_a9781641139526 _q(hardback) |
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020 |
_a9781641139533 _q(e-Book) |
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040 |
_aYDX _beng _cYDX _erda _epn _dN$T _dEBLCP _dOCLCF _dUCW _dTR-IsMEF |
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041 | 0 | _aeng | |
050 | 4 |
_aLB3060.55 _b.A66 2020 |
|
245 | 0 | 0 |
_aApplication of artificial intelligence to assessment / _cedited by Hong Jiao, University of Maryland, Robert W. Lissitz, University of Maryland. |
264 | 1 |
_aCharlotte : _bInformation Age Publishing, inc., _c2020. |
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264 | 4 | _c©2020 | |
300 |
_avi, 211 pages ; _bcharts ; _c25 cm. |
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336 |
_atext _2rdacontent |
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337 |
_aunmediated _2rdamedia |
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338 |
_avolume _2rdacarrier |
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490 | 1 | _aThe MARCES book series. | |
505 | 0 | _a1 Augmented intelligence and the future of item development. | |
505 | 0 | _a2 Reconceptualizing items: From clones and automatic item generation to task model families. | |
505 | 0 | _a3 Artificial intelligence for scoring oral reading fluency. | |
505 | 0 | _a4 Natural language processing and the literacy challenge. | |
505 | 0 | _a5 Practical considerations for using aI models in automated scoring of writing. | |
505 | 0 | _a6 Item pool design and assembly: The state of the art. | |
505 | 0 | _a7 Automated test assembly: Case studies in classical test theory and item response theory. | |
505 | 0 | _a8 Multistage testing in practice. | |
505 | 0 | _a9 An intelligent CAT that can deal with disengaged test taking. | |
505 | 0 | _a10 Differences in the Amount of adaptation exhibited by various computerized adaptive testing designs. | |
505 | 0 | _a11 Automatic item generation with machine learning techniques. | |
520 | 0 | _a"The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing" | |
650 | 7 | _aEducational tests and measurements | |
650 | 0 |
_aEducational tests and measurement _xData processing |
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650 | 7 | _aArtificial intelligence | |
650 | 0 |
_aExaminations _xDesign and construction |
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650 | 0 | _aComputer adaptive testing | |
700 | 1 |
_aJiao, Hong, _eeditor. |
|
700 | 1 |
_aLissitz, Robert W., _eeditor. |
|
830 | 0 | _aThe MARCES book series. | |
900 | _aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules | ||
910 | _aHomer Kitabevi | ||
942 |
_2lcc _cBKS |
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970 | 0 | 1 | _aContents. |
970 | 1 | 1 |
_l1 _tAugmented intelligence and the future of item development, _cMark J. Gierl, _fGierl, J. Mark, _p1. |
970 | 1 | 1 |
_l1 _tAugmented intelligence and the future of item development, _cHollis Lai, _fLai, Hollis, _p1. |
970 | 1 | 1 |
_l1 _tAugmented intelligence and the future of item development, _cDonna Matovinovic, _fMatovinovic, Donna, _p1. |
970 | 1 | 1 |
_l2 _tReconceptualizing items: From clones and automatic item generation to task model families, _cRichard Luecht, _fLuecht, Richard, _p25. |
970 | 1 | 1 |
_l2 _tReconceptualizing items: From clones and automatic item generation to task model families, _cMatthew Burke, _fBurke, Matthew, _p25. |
970 | 1 | 1 |
_l3 _tArtificial intelligence for scoring oral reading fluency, _cJared Bernstein, _fBernstein, Jared, _p51. |
970 | 1 | 1 |
_l3 _tArtificial intelligence for scoring oral reading fluency, _cJian Cheng, _fCheng, Jian, _p51. |
970 | 1 | 1 |
_l3 _tArtificial intelligence for scoring oral reading fluency, _cJennifer Balogh, _fBalogh, Jennifer, _p51. |
970 | 1 | 1 |
_l3 _tArtificial intelligence for scoring oral reading fluency, _cRyan Downey, _fDowney, Ryan, _p51. |
970 | 1 | 1 |
_l4 _tNatural language processing and the literacy challenge, _cJill Burstein, _fBurstein, Jill, _p77. |
970 | 1 | 1 |
_l5 _tPractical considerations for using AI models in automated, scoring of writing, _cPeter W. Foltz, _fFoltz, W. Peter, _p101. |
970 | 1 | 1 |
_l6 _tItem pool design and assembly: The state of the art, _cJeffrey M. Patton, _fPatton, M. Jeffrey, _p115. |
970 | 1 | 1 |
_l6 _tItem pool design and assembly: The state of the art, _cRay Y. Yan, _fYan, Y. Ray, _p115. |
970 | 1 | 1 |
_l7 _tAutomated test assembly: Case studies in classical test theory and item response theory, _cSiang Chee Chuah, _fChuah, Chee Siang, _p125. |
970 | 1 | 1 |
_l7 _tAutomated test assembly: Case studies in classical test theory and item response theory, _cDonovan Hare, _fHare, Donovan, _p125. |
970 | 1 | 1 |
_l7 _tAutomated test assembly: Case studies in classical test theory and item response theory, _cLuz Bay, _fBay, Luz, _p125. |
970 | 1 | 1 |
_l7 _tAutomated test assembly: Case studies in classical test theory and item response theory, _cThomas Proctor, _fProctor, Thomas, _p125. |
970 | 1 | 1 |
_l8 _tMultistage testing in practice, _cDuanli Yan, _fYna, Duanli, _p141. |
970 | 1 | 1 |
_l9 _tAn intellegent CAT that can deal with disengaged tets taking, _cSteven L. Wist, _fWist, L. Steven, _p161. |
970 | 1 | 1 |
_l10 _tDifferences in the amount of adaptation exhibited by various computerized adaptive testing designs, _cMark D. Reckase, _fReckase, D. Mark, _p175. |
970 | 1 | 1 |
_l10 _tDifferences in the amount of adaptation exhibited by various computerized adaptive testing designs, _cUnhee Ju, _fJu, Unhee, _p175. |
970 | 1 | 1 |
_l10 _tDifferences in the amount of adaptation exhibited by various computerized adaptive testing designs, _cSewon Kim, _fKim, Sewon, _p175. |
970 | 1 | 1 |
_l11 _tAutomatic item generation with machine learning techniques: A pathway to intelligent assessments, _cJaehwa Choi, _fChoi, Jaehwa, _p189. |
970 | 0 | 1 |
_aAbout the editors, _p211. |
999 |
_c25261 _d25261 |