在這需要學習知識的時代，建構一套有效的教學法是必要的，在衡量教學法產生的改變效益時，在早期通常是直接採用前後測總分的差異來評斷學生的改變狀況，而試題反應理論模型則考慮測量會有誤差，所以利用學生在前後測試題所量測的潛在能力上之改變來衡量教學成效。不管是Rasch模型或是延伸之後的廣義線性混合模式都已被廣泛使用於此，但這些模型常用的估計法要不是假設能力為常態之隨機變數，就是犧牲次要訊息去提高參數準確度。
此篇論文使用無母數貝氏分析中的Dirichlet Process(DP)先驗分配模型來考慮能力改變，目的即是將混合模型中對隨機效應的限制放鬆，允許隨機效應分布為無母數且具有分群效果。本研究經由模擬觀察到此模型相較於penalized quasi-likelihood(PQL)和條件最大概似法在隨機效應的估計上具有優勢，也發現當隨機效應維度變高時，甚至在固定效應估計上也較PQL 更為精確；另外在考慮如何選擇先驗分配方面，模擬結果建議可以藉由對於同樣是DP先驗分配概念下的兩個相似模型進行比對來挑選先驗分配。最後，我們利用DP先驗分配模型來探討及解釋實際資料中，不同教學法對學生的教學成效。

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