發(fā)布時(shí)間：2018-03-25 00:05

  本文選題：DINA模型　切入點(diǎn)：等值　出處：《江西師范大學(xué)》2014年碩士論文

【摘要】：認(rèn)知診斷理論將認(rèn)知診斷模型、認(rèn)知心理學(xué)和學(xué)科教學(xué)研究成果相結(jié)合,實(shí)現(xiàn)了對被試內(nèi)部心理加工成分、認(rèn)知過程、認(rèn)知成分或認(rèn)知加工的測量。與經(jīng)典測量理論和項(xiàng)目反應(yīng)理論相比,能夠提供更加詳細(xì)的診斷信息,已經(jīng)成為心理與教育測量學(xué)界最重要的研究領(lǐng)域之一。然而,要在大規(guī)模評價(jià)活動(dòng)中應(yīng)用認(rèn)知診斷理論,首先需要解決的就是測驗(yàn)等值的問題。等值是進(jìn)行大規(guī)模評價(jià)中經(jīng)常用到的測量技術(shù),以實(shí)現(xiàn)測驗(yàn)分?jǐn)?shù)間的可比性為目的。 在眾多的認(rèn)知診斷模型中,DINA模型(Deterministic Inputs, Noisy and Gate Model)由于其在擁有簡潔的項(xiàng)目參數(shù)的同時(shí),診斷準(zhǔn)確率較高,已成為目前被應(yīng)用的較為廣泛的認(rèn)知診斷模型之一。De la Torre和Lee(2010)指出,DINA模型(Haertel,1989; Junker和Sijtsma,2001)只有在以下兩種情形里沒有進(jìn)行等值的必要：第一,模型和被試反應(yīng)數(shù)據(jù)完全擬合；第二,兩次測試下的被試特質(zhì)分布大致相同(如屬性分布類似等)。但是,在實(shí)際測驗(yàn)情境中,這兩種條件基本無法滿足。這也就成為了在基于DINA模型的大規(guī)模的心理或教育評價(jià)活動(dòng)中需要進(jìn)行等值的原因。本研究旨在探索DINA模型里等值的邏輯關(guān)系,為建立DINA模型的等值轉(zhuǎn)換關(guān)系式打下基礎(chǔ)。 本研究分為6個(gè)實(shí)驗(yàn),分別探討了DINA模型中的項(xiàng)目的失誤參數(shù)s與猜測參數(shù)g、被試的IMP的掌握概率與項(xiàng)目的失誤參數(shù)s、被試的IMP的掌握概率與項(xiàng)目的猜測參數(shù)g之間的相互關(guān)系。 主要研究結(jié)論如下： (1)項(xiàng)目的失誤參數(shù)s與猜測參數(shù)g之間相互獨(dú)立； (2)被試的IMP的掌握概率的變化(x)對項(xiàng)目j的失誤參數(shù)S(ygi)的影響為：其中,sj為被試的IMP的掌握概率未變化時(shí)項(xiàng)目j的失誤參數(shù)s的值。 (3)被試的IMP的掌握概率的變化(x)對項(xiàng)目j的猜測參數(shù)g(ygi)的影響為：其中,.9j為被試的IMP的掌握概率未變化時(shí)項(xiàng)目j的猜測參數(shù)g的值。 (4)項(xiàng)目j的失誤參數(shù)s的變化(x)對被試的IMP的掌握概率(YPAi)的影響為：其中,PAi：項(xiàng)目j的失誤參數(shù)s未變化時(shí)被試的IMP的掌握概率的值。 (5)項(xiàng)目j的猜測參數(shù)g的變化(x)對被試的IMP的掌握概率(YPAi)的影響為：其中,PAi：項(xiàng)目j的猜測參數(shù)g未變化時(shí)被試的IMP的掌握概率的值。
[Abstract]:The cognitive diagnosis theory combines the cognitive diagnosis model, cognitive psychology and the research results of subject teaching and learning to realize the mental processing and cognitive process of the subjects. The measurement of cognitive component or cognitive processing, which can provide more detailed diagnostic information than classical measurement theory and item response theory, has become one of the most important research fields in the field of psychological and educational measurement. In order to apply the cognitive diagnosis theory to the large-scale evaluation, the problem of test equivalence, which is often used in large-scale evaluation, is the first problem to be solved in order to achieve the comparability between test scores. Among the many cognitive diagnostic models, Noisy and Gate models (Noisy and Gate models) have higher diagnostic accuracy because of their concise project parameters. One of the more widely used cognitive diagnostic models. De la Torre and Leehs2010) points out that the Dina model is Haertell 1989; Junker and Sijtsmai 2001).) there is no need for equivalence only in the following two situations: first, the model and the test response data are fully fitted; and second, there is no need for equivalence in the following two cases: first, the model and the data of the test response are completely fitted; second, The distribution of traits in the two tests was roughly the same (such as similar distribution of attributes, etc.). However, in the actual test situation, These two conditions can not be satisfied, which is the reason for the need for equivalence in large-scale psychological or educational evaluation activities based on the DINA model. The purpose of this study is to explore the logical relationship of equivalence in the DINA model. It lays the foundation for establishing the equivalent transformation relation of DINA model. This study was divided into six experiments. The relationship between the project error parameter s and guess parameter g in DINA model, the grasping probability of IMP and the error parameter s of the project, the grasp probability of IMP and the guess parameter g of the project are discussed respectively. The main findings are as follows:. (1) the error parameter s and the guess parameter g are independent of each other; (2) the influence of the change of the mastery probability of IMP on the error parameter of item j is: where Sj is the value of the parameter s of item j when the probability of mastery of IMP does not change. (3) the influence of the change of the mastery probability of IMP on the guess parameter of item j is: the value of the guess parameter g of item j is the value of the item j when the mastery probability of IMP of the subject does not change. (4) the influence of the error parameter s of item j on the mastery probability of IMP was as follows: the value of mastery probability of IMP when the error parameter s of item j did not change. (5) the influence of the change of the guess parameter g of item j on the mastery probability of IMP of the subjects is as follows: the value of the mastery probability of the IMP of the subjects when the guess parameter g of item j does not change.
【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】：2014
【分類號】：B842.1

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