多級評分的多維計算機化自適應(yīng)測驗選題策略研究
發(fā)布時間：2018-10-19 13:45
【摘要】：多維計算機化自適應(yīng)測驗(MCAT)將計算機化自適應(yīng)測驗(CAT)與多維項目反應(yīng)理論(MIRT)相結(jié)合,以盡可能多得獲取被試的多維特質(zhì)信息為目的,在保證測驗準(zhǔn)確高效的同時,還具有從被試反應(yīng)中獲取診斷信息的潛力。多級評分項目因能提供更多信息并可測量更復(fù)雜的能力和技能而被廣泛應(yīng)用。然而,目前大多數(shù)MCAT算法技術(shù)是以M3PL或M2PL模型為條件的,這些算法與技術(shù)可能并不適用于多級評分模型。本文的目的是探討將MCAT中的選題策略拓展到PMCAT中,并開發(fā)出新的選題策略。本研究還進(jìn)一步探索了測驗維度數(shù)、維度間的相關(guān)大小如何影響PMCAT的準(zhǔn)確性和安全性。一些常用的MCAT選題策略——包括基于FI的D-優(yōu)化、A-優(yōu)化、E-優(yōu)化及其貝葉斯版本;基于傳統(tǒng)KL信息量的KI方法、后驗期望KL信息方法(KB);以及基于后驗分布間KL距離的KLP方法、互信息(MUI)方法和連續(xù)熵方法(CEM)都被拓展以適合于多級評分的項目。通過將CEM算法中后驗概率的計算公式中預(yù)設(shè)的固定的先驗概率替換為隨著測驗不斷更新的當(dāng)前后驗概率,對原有CEM方法進(jìn)行了改進(jìn)。然后展開了兩項Monte Carlo模擬研究:一是驗證了PMCAT的可行性(研究二),并比較了各種選題策略間的表現(xiàn);二是進(jìn)一步探索了能力維度數(shù)(p=2 and 5)以及維度間相關(guān)大小(r=0,0.2,0.5 and 0.8)這兩個因素對估計精度及項目曝光率的影響(研究三)。本研究使用的多級評分模型為多維等級反應(yīng)模型(MGREM),選擇EAP為測驗進(jìn)行中的潛在特質(zhì)估計方法,測驗終止條件設(shè)置為定長。模擬試驗表明,拓展的PMCAT選題策略基本合理、可行,本文開發(fā)的新選題策略(MCEM)整體表現(xiàn)最好。研究發(fā)現(xiàn):(1)大多數(shù)選題策略的估計誤差隨著維度數(shù)的增加變大,而由2維到5維時,KI方法的估計精度提升了;(2)維度間相關(guān)只在中等強度以上時才對選題策略的估計精度有影響,KI方法的估計誤差隨著維度間相關(guān)增加顯著下降;(3)維度數(shù)量越多,維度間的相關(guān)越高,項目曝光率越低。特別是A-優(yōu)化方法,在2維時曝光率最高,在5維時其曝光率下降到最低。多級評分項目廣泛應(yīng)用于李克特式評分的心理測量量表和成就測驗中。采用多級評分項目的MCAT具有廣闊的應(yīng)用前景。對于PMCAT選題策略的拓展可供理論研究和實際應(yīng)用參考。
[Abstract]:The Multidimensional computerized Adaptive Test (MCAT) combines the computerized Adaptive Test (CAT) with the Multidimensional item response Theory (MIRT) in order to obtain as much multidimensional trait information as possible, while ensuring the accuracy and efficiency of the test. It also has the potential to obtain diagnostic information from subjects' reactions. Multilevel scoring is widely used because of its ability to provide more information and to measure more complex competencies and skills. However, most of the current MCAT algorithms are based on M3PL or M2PL models, and these algorithms and techniques may not be suitable for multilevel scoring models. The purpose of this paper is to explore how to extend the topic selection strategy in MCAT to PMCAT and develop a new topic selection strategy. The study also explores how the number of dimensions and the correlation between dimensions affect the accuracy and security of PMCAT. Some common MCAT selection strategies include D- optimization, A- optimization, E- optimization and Bayesian version based on FI, KI method based on traditional KL information content, (KB); method of posterior expectation KL information and KLP method based on KL distance between posteriori distributions. Both the mutual information (MUI) method and the continuous entropy method, (CEM), are extended to suit multilevel scoring items. By replacing the fixed prior probability of the posteriori probability in the calculation formula of CEM algorithm with the current posteriori probability which is updated continuously with the test, the original CEM method is improved. Then two Monte Carlo simulation studies are carried out: one is to verify the feasibility of PMCAT (study 2), and to compare the performance of various topics; The second is to further explore the influence of two factors, the capability dimension (pt2 and 5) and the correlation between dimensions (r 0. 2 0. 2 0. 5 and 0. 8) on the estimation accuracy and item exposure (study 3). The multi-level rating model used in this study is a multi-dimensional rating response model (MGREM),). EAP is chosen as the potential trait estimation method in the test, and the test termination condition is set to a fixed length. The simulation results show that the extended PMCAT selection strategy is reasonable and feasible, and the new topic selection strategy (MCEM) developed in this paper is the best overall performance. The results show that: (1) the estimation errors of most of the selection strategies increase with the increase of the number of dimensions. From 2 to 5 dimensions, the estimation accuracy of KI method is improved. (2) Interdimensional correlation only affects the estimation accuracy of the selection strategy when the correlation is more than moderate intensity. The estimation error of KI method decreases significantly with the increase of interdimensional correlation. (3) the more dimension, the more dimension. The higher the correlation between dimensions, the lower the item exposure. Especially, the A- optimization method has the highest exposure at 2 D and the lowest exposure at 5 D. Multi-level scoring is widely used in Richter scale and achievement test. MCAT with multilevel scoring items has a broad application prospect. The development of PMCAT topic selection strategy can be used as a reference for theoretical research and practical application.
【學(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：B841
[Abstract]:The Multidimensional computerized Adaptive Test (MCAT) combines the computerized Adaptive Test (CAT) with the Multidimensional item response Theory (MIRT) in order to obtain as much multidimensional trait information as possible, while ensuring the accuracy and efficiency of the test. It also has the potential to obtain diagnostic information from subjects' reactions. Multilevel scoring is widely used because of its ability to provide more information and to measure more complex competencies and skills. However, most of the current MCAT algorithms are based on M3PL or M2PL models, and these algorithms and techniques may not be suitable for multilevel scoring models. The purpose of this paper is to explore how to extend the topic selection strategy in MCAT to PMCAT and develop a new topic selection strategy. The study also explores how the number of dimensions and the correlation between dimensions affect the accuracy and security of PMCAT. Some common MCAT selection strategies include D- optimization, A- optimization, E- optimization and Bayesian version based on FI, KI method based on traditional KL information content, (KB); method of posterior expectation KL information and KLP method based on KL distance between posteriori distributions. Both the mutual information (MUI) method and the continuous entropy method, (CEM), are extended to suit multilevel scoring items. By replacing the fixed prior probability of the posteriori probability in the calculation formula of CEM algorithm with the current posteriori probability which is updated continuously with the test, the original CEM method is improved. Then two Monte Carlo simulation studies are carried out: one is to verify the feasibility of PMCAT (study 2), and to compare the performance of various topics; The second is to further explore the influence of two factors, the capability dimension (pt2 and 5) and the correlation between dimensions (r 0. 2 0. 2 0. 5 and 0. 8) on the estimation accuracy and item exposure (study 3). The multi-level rating model used in this study is a multi-dimensional rating response model (MGREM),). EAP is chosen as the potential trait estimation method in the test, and the test termination condition is set to a fixed length. The simulation results show that the extended PMCAT selection strategy is reasonable and feasible, and the new topic selection strategy (MCEM) developed in this paper is the best overall performance. The results show that: (1) the estimation errors of most of the selection strategies increase with the increase of the number of dimensions. From 2 to 5 dimensions, the estimation accuracy of KI method is improved. (2) Interdimensional correlation only affects the estimation accuracy of the selection strategy when the correlation is more than moderate intensity. The estimation error of KI method decreases significantly with the increase of interdimensional correlation. (3) the more dimension, the more dimension. The higher the correlation between dimensions, the lower the item exposure. Especially, the A- optimization method has the highest exposure at 2 D and the lowest exposure at 5 D. Multi-level scoring is widely used in Richter scale and achievement test. MCAT with multilevel scoring items has a broad application prospect. The development of PMCAT topic selection strategy can be used as a reference for theoretical research and practical application.
【學(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：B841
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 韓雨婷;涂冬波;王瀟o，
本文編號：2281305
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