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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/9468

    Title: 基於GHMM之核平滑化無參數IRT教育測驗分析模型之研究(I)
    Authors: 劉湘川
    Contributors: 健康學院
    Keywords: 廣義隱藏式馬可夫模型
    Generalized Hidden Markov Model
    Item Response Theory
    Item Relationship Structure
    Ordering Theory
    Kernel smoothing Nonparametric IRT
    Date: 2006
    Issue Date: 2010-05-07 15:49:00 (UTC+8)
    Abstract: 現代測驗試題反應理論眾所周知且廣為應用之三參數洛基IRT 模式在教育測驗的實際應用上具有「有測驗試題局部獨立之限制」、「不適用於非時間序列測驗」、「並未考慮受試者遺漏作答或未予作答情況」、「需要極大之樣本,才可得穩定可靠之參數估計值」等四種限制,這些限制也影響其適用性。
    本研究計畫主要目的即擬在作者多年持續研究發展之既有成果基礎下,結合以作者先前所發展之「基於廣義隱藏式馬可夫模型之教育測驗分析模型」,與作者已提出之「改進之核平滑化無參數試題反應理論模式」之兩種模式,發展出兼顧上述四種限制,可彈性適用各種教育測驗之整合新模型;「基於廣義隱藏式馬可夫模型兼顧猜測及未作答之核平滑化無參數試題反應理論模式」及其便利使用之電腦程式,並以蒙地卡羅模擬研究法,進行模擬發展之完備整合模型;「基於GHMM 之核平滑化無參數IRT 教育測驗分析模型」,與既有常用之「三參數洛基IRT 分析模型」、及「改進之核平滑化無參數試題反應理論分析模式(KN-IRT)」大量資料之模擬實驗比較,以驗證作者所擬發展之該整合新模型之優越性,進而提出其與「試題關聯結構法(IRS)」或「順序理論(OT)」之整合應用模式,期能使試題間架構或順序性之估計更臻完美。

    There are four deficiencies in the 3PL model of modern item response theory. First, localindependent assumption is made, that is, the relationship between items is ignored. It, hence,
    can’t to use to estimation the structure between items. Second, it can’t apply to time seriescases directly. Third, missing data processing is neglected, so it is unsuitable for educational
    measurement. Fourth, satisfactory parameters estimation depends on quite a few samples. Itcan be introduced to large scale entrance examination only.
    The main purpose of this study is to integrate a hybrid model of generalized hiddenmarkov model (GHMM) and kernel smoothing nonparametric IRT (KN-IRT) to conquer these four constraints. Such a model can be used for item analysis of both series correlation andseries non correlation cases. Furthermore, this model can be applied to item ordering theory(OT) or item relational structure (IRS) to enhance the structure between items more precisely.
    Appears in Collections:[生物科技學系] 科技部研究計畫

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