肆 肆
肆、 、 、 、估計模式的影響 估計模式的影響 估計模式的影響 估計模式的影響
由模擬樣本資料得知，不論在試題參數與概念辨識率的估計上，DINA 模式 都比 G-DINA 模式良好。應是 G-DINA 模式在估計時受到概念彼此間影響的關 係，而無法精準地判斷純以 DINA 模式產生的模擬樣本資料；但在樣本數較小時，
又因其考慮關係因素較多，當 DINA 模式估計出現無法收斂的情形時，G-DINA 模式則能確實完成估計。
第二節 第二節 第二節
第二節 未來研究建議 未來研究建議 未來研究建議 未來研究建議
茲根據本研究部分未盡完善之處，提出以下具體研究建議，以供後續相關研 究之參考。
一、 不同的樣本數
本研究設定樣本人數為 100 人、500 人、以及 1000 人三種，其試題參數與概 念辨識率皆有明顯變好的趨勢，後續可將樣本數提高為 1500 人或 2000 人來探討 其估計效果。
二、 不同的認知屬性分佈
本研究探討了 HO-DINA 模式、MVN(0)分配、MVN(0.5)分配、以及 uniform 分配…等四種不同的認知屬性分佈，在 HO-DINA 模式方面，試題參數與概念辨 識率有較明顯的差異；而 MVN(0)分配與 MVN(0.5)分配方面，則是比較符合本研
究就所預期的試題參數不變性的特性。後續可針對各種不同多變量常態分配的方 法來探討其估計效果。
三、 不同的能力分佈
本研究把各種認知屬性的受試者皆分為低能力組、中能力組、以及高能力 組，在低能力組方面，試題參數與概念辨識率的估計顯得較差；而中能力組方面，
試題參數與概念辨識率的估計有些微的提升；在高能力組方面，試題參數與概念 辨識率的估計雖然有較好的估計值，但也未能達到 de la Torre & Lee(2010)的研究 結果。後續可針對更多不同能力分佈的差異，例如：正偏態分佈、負偏態分佈、
或雙峰分佈…等來探討其估計效果。
四、 試題參數的估計
從本研究的結果來看，不論模擬樣本資料的產生方式為 HO-DINA 模式或多 變量常態分配方法，其粗心機率(s 參數)的估計上皆有較明顯差異，是因為受試者 能力分佈高低不同的影響，後續可針對此部分的差異進行深入研究與探討。另 外，本研究將試題參數設定為 g=0.1、s=0.1，後續可廣泛討論不同試題參數的組 合來探討其估計效果，如：g=0.2、s=0.1 或 g=0.1、s=0.2。
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