Applications of Bayesian Decision Theory to Sequential Mastery Testing

Applications of Bayesian Decision Theory to Sequential Mastery Testing

The purpose of this paper is to formulate optimal sequential rules for mastery tests. The framework for the approach is derived from Bayesian sequential decision theory. Both a threshold and linear loss structure are considered. The binomial probability distribution is adopted as the psychometric mo...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:Journal of Educational and Behavioral Statistics. - SAGE Publishing, 1976. - 24(1999), 3, Seite 271-292
1. Verfasser: Vos, Hans J. (VerfasserIn)
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 1999
Zugriff auf das übergeordnete Werk:Journal of Educational and Behavioral Statistics
Schlagworte:Bayesian decision theory Beta-binomial model Dynamic programming Monotonicity conditions Sequential mastery testing Behavioral sciences Education Mathematics Health sciences
LEADER 01000caa a22002652 4500
001 JST047115483
003 DE-627
005 20240621131846.0
007 cr uuu---uuuuu
008 150324s1999 xx |||||o 00| ||eng c
035 |a (DE-627)JST047115483 
035 |a (JST)1165325 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Vos, Hans J.  |e verfasserin  |4 aut 
245 1 0 |a Applications of Bayesian Decision Theory to Sequential Mastery Testing 
264 1 |c 1999 
336 |a Text  |b txt  |2 rdacontent 
337 |a Computermedien  |b c  |2 rdamedia 
338 |a Online-Ressource  |b cr  |2 rdacarrier 
520 |a The purpose of this paper is to formulate optimal sequential rules for mastery tests. The framework for the approach is derived from Bayesian sequential decision theory. Both a threshold and linear loss structure are considered. The binomial probability distribution is adopted as the psychometric model involved. Conditions sufficient for sequentially setting optimal cutting scores are presented. Optimal sequential rules will be derived for the case of a subjective beta distribution representing prior true level of functioning. An empirical example of sequential mastery testing for concept-learning in medicine concludes the paper. 
540 |a Copyright 1999 The American Educational Research Association and the American Statistical Association 
650 4 |a Bayesian decision theory 
650 4 |a Beta-binomial model 
650 4 |a Dynamic programming 
650 4 |a Monotonicity conditions 
650 4 |a Sequential mastery testing 
650 4 |a Behavioral sciences  |x Psychology  |x Cognitive psychology  |x Decision theory 
650 4 |a Education  |x Formal education  |x Pedagogy  |x Educational methods  |x Educational testing  |x Educational tests  |x Achievement tests  |x Mastery tests 
650 4 |a Mathematics  |x Pure mathematics  |x Algebra  |x Polynomials  |x Binomials 
650 4 |a Behavioral sciences  |x Psychology  |x Psychometrics 
650 4 |a Mathematics  |x Applied mathematics  |x Statistics 
650 4 |a Education  |x Formal education  |x Academic education  |x Academic achievement  |x Academic accomplishments  |x Educational attainment  |x Prior learning 
650 4 |a Mathematics  |x Mathematical analysis  |x Mathematical monotonicity 
650 4 |a Mathematics  |x Mathematical procedures 
650 4 |a Health sciences  |x Health care industry  |x Health care administration 
650 4 |a Behavioral sciences  |x Psychology  |x Cognitive psychology  |x Cognitive processes  |x Decision making  |x Backward induction 
655 4 |a research-article 
773 0 8 |i Enthalten in  |t Journal of Educational and Behavioral Statistics  |d SAGE Publishing, 1976  |g 24(1999), 3, Seite 271-292  |w (DE-627)477533302  |w (DE-600)2174169-4  |x 19351054  |7 nnns 
773 1 8 |g volume:24  |g year:1999  |g number:3  |g pages:271-292 
856 4 0 |u https://www.jstor.org/stable/1165325  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_JST 
912 |a GBV_ILN_11 
912 |a GBV_ILN_20 
912 |a GBV_ILN_22 
912 |a GBV_ILN_23 
912 |a GBV_ILN_24 
912 |a GBV_ILN_26 
912 |a GBV_ILN_31 
912 |a GBV_ILN_32 
912 |a GBV_ILN_39 
912 |a GBV_ILN_40 
912 |a GBV_ILN_60 
912 |a GBV_ILN_62 
912 |a GBV_ILN_63 
912 |a GBV_ILN_65 
912 |a GBV_ILN_69 
912 |a GBV_ILN_70 
912 |a GBV_ILN_73 
912 |a GBV_ILN_74 
912 |a GBV_ILN_90 
912 |a GBV_ILN_95 
912 |a GBV_ILN_100 
912 |a GBV_ILN_101 
912 |a GBV_ILN_105 
912 |a GBV_ILN_110 
912 |a GBV_ILN_120 
912 |a GBV_ILN_121 
912 |a GBV_ILN_138 
912 |a GBV_ILN_150 
912 |a GBV_ILN_151 
912 |a GBV_ILN_152 
912 |a GBV_ILN_161 
912 |a GBV_ILN_165 
912 |a GBV_ILN_171 
912 |a GBV_ILN_187 
912 |a GBV_ILN_206 
912 |a GBV_ILN_213 
912 |a GBV_ILN_224 
912 |a GBV_ILN_230 
912 |a GBV_ILN_250 
912 |a GBV_ILN_281 
912 |a GBV_ILN_285 
912 |a GBV_ILN_293 
912 |a GBV_ILN_370 
912 |a GBV_ILN_374 
912 |a GBV_ILN_602 
912 |a GBV_ILN_636 
912 |a GBV_ILN_647 
912 |a GBV_ILN_702 
912 |a GBV_ILN_2001 
912 |a GBV_ILN_2003 
912 |a GBV_ILN_2005 
912 |a GBV_ILN_2006 
912 |a GBV_ILN_2007 
912 |a GBV_ILN_2008 
912 |a GBV_ILN_2009 
912 |a GBV_ILN_2010 
912 |a GBV_ILN_2011 
912 |a GBV_ILN_2014 
912 |a GBV_ILN_2015 
912 |a GBV_ILN_2018 
912 |a GBV_ILN_2020 
912 |a GBV_ILN_2021 
912 |a GBV_ILN_2025 
912 |a GBV_ILN_2026 
912 |a GBV_ILN_2027 
912 |a GBV_ILN_2031 
912 |a GBV_ILN_2034 
912 |a GBV_ILN_2036 
912 |a GBV_ILN_2037 
912 |a GBV_ILN_2038 
912 |a GBV_ILN_2039 
912 |a GBV_ILN_2043 
912 |a GBV_ILN_2044 
912 |a GBV_ILN_2048 
912 |a GBV_ILN_2049 
912 |a GBV_ILN_2050 
912 |a GBV_ILN_2055 
912 |a GBV_ILN_2056 
912 |a GBV_ILN_2057 
912 |a GBV_ILN_2059 
912 |a GBV_ILN_2061 
912 |a GBV_ILN_2064 
912 |a GBV_ILN_2065 
912 |a GBV_ILN_2068 
912 |a GBV_ILN_2070 
912 |a GBV_ILN_2086 
912 |a GBV_ILN_2088 
912 |a GBV_ILN_2093 
912 |a GBV_ILN_2098 
912 |a GBV_ILN_2106 
912 |a GBV_ILN_2107 
912 |a GBV_ILN_2108 
912 |a GBV_ILN_2110 
912 |a GBV_ILN_2111 
912 |a GBV_ILN_2112 
912 |a GBV_ILN_2113 
912 |a GBV_ILN_2116 
912 |a GBV_ILN_2118 
912 |a GBV_ILN_2119 
912 |a GBV_ILN_2122 
912 |a GBV_ILN_2125 
912 |a GBV_ILN_2129 
912 |a GBV_ILN_2143 
912 |a GBV_ILN_2144 
912 |a GBV_ILN_2145 
912 |a GBV_ILN_2147 
912 |a GBV_ILN_2148 
912 |a GBV_ILN_2152 
912 |a GBV_ILN_2153 
912 |a GBV_ILN_2158 
912 |a GBV_ILN_2190 
912 |a GBV_ILN_2193 
912 |a GBV_ILN_2232 
912 |a GBV_ILN_2336 
912 |a GBV_ILN_2446 
912 |a GBV_ILN_2470 
912 |a GBV_ILN_2507 
912 |a GBV_ILN_2522 
912 |a GBV_ILN_2548 
912 |a GBV_ILN_2705 
912 |a GBV_ILN_2889 
912 |a GBV_ILN_2890 
912 |a GBV_ILN_2935 
912 |a GBV_ILN_2947 
912 |a GBV_ILN_2949 
912 |a GBV_ILN_2950 
912 |a GBV_ILN_4012 
912 |a GBV_ILN_4035 
912 |a GBV_ILN_4037 
912 |a GBV_ILN_4046 
912 |a GBV_ILN_4112 
912 |a GBV_ILN_4125 
912 |a GBV_ILN_4126 
912 |a GBV_ILN_4242 
912 |a GBV_ILN_4246 
912 |a GBV_ILN_4249 
912 |a GBV_ILN_4251 
912 |a GBV_ILN_4277 
912 |a GBV_ILN_4305 
912 |a GBV_ILN_4306 
912 |a GBV_ILN_4307 
912 |a GBV_ILN_4313 
912 |a GBV_ILN_4322 
912 |a GBV_ILN_4323 
912 |a GBV_ILN_4324 
912 |a GBV_ILN_4325 
912 |a GBV_ILN_4326 
912 |a GBV_ILN_4328 
912 |a GBV_ILN_4333 
912 |a GBV_ILN_4335 
912 |a GBV_ILN_4338 
912 |a GBV_ILN_4346 
912 |a GBV_ILN_4367 
912 |a GBV_ILN_4393 
912 |a GBV_ILN_4700 
912 |a GBV_ILN_4753 
951 |a AR 
952 |d 24  |j 1999  |e 3  |h 271-292