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題名：題組與多向度電腦適性測驗之選題策略的比較
作者：施慶麟 引用關係
作者(外文)：Ching-Lin Shih
校院名稱：國立中正大學
系所名稱：心理學所
指導教授：王文中
學位類別：博士
出版日期：2008
主題關鍵詞：電腦適性測驗；題組反應理論；多向度試題反應理論；題組；選題策略；item selection strategy；computerized adaptive testing；testlet response theory；multidimensional item response theory；testlet
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適性測驗是針對不同的受試者提供符合其能力的試題，避免受試者浪費時間在作答過難或過易的試題，因此每位受試者皆有其個人化的試題組合，藉以精準測量其能力。由於適性測驗的目標是每個施測的試題都盡可能的接近受試者能力，因此在選擇供受試者施測的試題時較為耗費時間。基於提昇適性測驗效率的目的，電腦科技被引進至測驗領域並應用於協助提高測驗效率，所以電腦適性測驗已經成為近20年來諸多學者探究的議題。然而以往探究的主題多數築基於單向度試題反應理論的基礎上，進行相關議題的研究，但隨著不同類型試題的採用以及適用於不同測驗情境之試題反應理論模式的發展，多向度電腦適性測驗（MCAT）以及題組式電腦適性測驗（TCAT）將可能是未來的發展方向。故此，本研究在MCAT及TCAT情境下，針對Fisher information（FI）、Fisher interval information（FII）、Fisher information with a posterior distribution（FIP）、Kullback-Leibler information（KL）以及Kullback-Leibler information with a posterior distribution（KLP）等5種選題策略，在不考量真實情境中如曝光率控制、內容平衡等限制的情形下，進行模擬實驗的比較。
在MCAT部分，本研究操弄（1）MIRT模式；（2）題庫規模；（3）向度間關連；（4） 選題策略等4個獨變項，探究5種選題策略在不同實驗設計下於（1）受試者能力參數估計值與真值間的偏誤（bias）；（2）能力估計的RMSE（root mean square error）；（3）題庫使用率（bank usage）；以及（4）測驗重複率（average between tests overlap rate）等依變項上的表現。結果發現在M1PL的模式下，FI、KL與KLP等三種策略在能力估計的精準度上並無明顯差異，但由於FI的題庫使用比率高於KL及KLP，因此建議在M1PL時應採用FI作為MCAT的選題策略；在M3PL的模式下，若向度間相關較低，建議採用FII或FIP作為選題策略，可兼顧能力估計以及題庫使用情形；若向度間的相關較高時，則建議採用FI作為MCAT系統的選題策略。
在TCAT的情境中，本研究主張題組內的試題難度分配情形對於CAT系統的效能應該有所影響，因此將題組結構納入探討的獨變項之一，此外還包含（1）TRT模式；（2）題庫規模；（3）題組內的試題個數及題組測驗長度；（4）題組效應；（5）選題策略等5種獨變項。依變項部分則與MCAT部分相同。研究結果發現，在1PTL下，本研究建議在測驗前期可使用KL或KLP選題策略，後期則使用FI選題，可得到略為準確的能力估計；在3PTL情境下，考量5種策略在能力估計的RMSE以及題庫使用率上的表現，本研究建議不論題組內的難度分配為何，採用FII或FIP為選題策略是較理想的選擇。
Adaptive testing can increase measurement efficiency by providing items that tailored to each individual examinee and prevent the examinee from wasting time to answer the items that are either too easy or too difficult. Since the tests are tailored for each examinee, a precise estimate of his or her ability can be expected. Due to the goal of adaptive testing is to select the item that approximates respondent’s ability as close as possible, it takes times to find out the most appropriate item for the examinee. To improve the efficiency of adaptive testing, computer technology was leaded in the adaptive testing. In the past two decades, issues about computerized adaptive testing have become more and more popular. However, most of the studies were mainly based on unidimensional item response theory. As different types of items (e.g., multidimensional items and testlet-based items) were used in the real test situations and the corresponding models were further established, multidimensional computerized adaptive testing (MCAT) or testlet-based computerized adaptive testing (TCAT) could be constructed and should be studied. This study investigates the performance of different item selection strategies under MCAT and TCAT. Five strategies (Fisher information; FI, Fisher interval information; FII, Fisher information with a posterior distribution; FIP, Kullback-Leibler information; KL, Kullback-Leibler information with a posterior distribution; KLP) were compared here using simulated data without taking exposure control and content balancing into consideration.
Four independent variables were manipulated under the MCAT part: (a) the MIRT models (M1PL and M3PL); (b) the size of item bank; (c) the correlation coefficient between dimensions; and (d) the item selection strategies. Dependent variables were: (a) the bias of the ability estimates; (b) the root mean square error (RMSE) of the ability estimates; (c) the rate of item bank usage; and (d) the average between tests overlap rate. The results showed that the differences in RMSEs between FI, KL, and KLP could be neglected under the M1PL model, but FI was recommended for higher rate of bank usage. Under the M3PL model, FII and FIP should be adopted when the correlation between dimensions is low, whereas the FI should be adopted when strong correlation is found between dimensions.
Under the framework of TCAT, the performance seems to be affected by the distribution of the item difficulty within each testlet. Therefore the structure of item difficulty within testlet was taken as an independent variable in this study. In addition, five more independent variables were manipulated in this study: (a) the TRT model (1PTL and 3PTL); (b) the size of item bank; (c) the number of items within each testlet; (d) the effect of testlet; and (e) the item selection strategies. Dependent variables used here were the same as those used under the MCAT part. It was found that under the 1PTL, KL and KLP should be used in the early stage of TCAT, whereas the FI should be adopted in the following testing stage for a more precise estimation of respondent’s ability. Taking both the RMSE of ability estimates and the rate bank usage into consideration, FII and FIP were suggested under the 3PTL model, no matter which distribution of item difficulty within testlet is.
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