以a-鄰近法爲選題策略之電腦化適性測驗模擬研究

A Computerized Adaptive Testing System with the a-NN Method as Item Selection Strategy

10.7108/PT.200912.0605

錢永財(Yung-Tsai Chien)；郭伯臣(Bor-Chen Kuo)；陳曉竹(Hsiao-Chu Chen)

試題反應理論 ； 電腦適性測驗 ； 選題策略 ； 曝光率控制 ； computerized adaptive testing ； item exposure control ； item response theory ； selection strategy

測驗學刊

56卷4期（2009 / 12 / 01）

605 - 638

繁體中文

試題曝光率的控制對電腦適性測驗的題庫安全是很重要的，如不能有效控制，將因而使得性質良好的試題過度曝光，進而影響測驗的安全性與公平性。若能有效分散試題，平均各試題的使用率，對整體試題之曝光率將可有效控制，進而可有效降低題庫中高曝光率試題之題數，達到提升題庫使用效益的目的。 本研究分別提出「測驗初期b值分層隨機選取法」與「a-鄰近法」，並進行兩者的比較，試圖找出在有效控制試題曝光率時，能力估計誤差損失較小的選題方法。 根據模擬研究的結果發現： 1.電腦適性測驗前期採取b值分層隨機選取法進行選題，將可：(1)有效控制題庫中未使用的試題題數，提升題庫建制之經濟效益；(2)降低試題曝光率，延長題庫使用時限。 2.a-NN選題法在題庫曝光率的控制上，可達到：(1)有效降低題庫中高曝光率試題之曝光率；(2)有效提升題庫中未選用試題的使用率；(3)不論在模擬試題參數題庫，或是真實試題參數題庫中，皆可有效控制題庫試題曝光率。

To prevent the items over exposure is very important to the safety of item pool. Without controlling the exposure of items, the security and fairness of the test might not be maintained. The item bank will be used efficiently if the frequencies of each item being administered were similar. Two methods, b-stratified random selection method in the early stage of CAT and a-NN method, were proposed by this study. Through simulation studies, the effect of item exposure control on ability estimation was compared between these two methods. The conclusions of study were: 1. The b-stratified random selection method in the early stage of CAT was found useful on: (1) controlling the number of items that never been administered and improve the efficiency of the item pool, and (2) reducing the item exposure rates and lengthen the usage of item pool. 2. The a-NN method has better performance on: (1) reduced the exposure rate of the over exposure items in the pool effectively, (2) improved the usage of items that never administered, and (3) effectively control the exposure rate of items in the item pool, no matter in simulation item pools or real ones.

1. 謝友詩(2005)。碩士論文(碩士論文)。台中市，國立台中師範學院數學教育系。
   連結：
2. Baker, F. B.(1990).Some observations on the metric of PC-BILOG results.Applied psychological measurement,14,139-150.
3. Birnbaum, A.,F. M. Lord (Eds.),M. R. Novick (Eds.)(1968).Statistical theories of mental test scores.Reading, MA:Addison-Wesley.
4. Chang, H. H.,Ying, Z.(1996).A global information approach to computerized adaptive testing.Applied Psychological Measurement,20(3),231-229.
5. Cheng, P. E.,Liou, M.(2003).Computerized adaptive testing using the nearest neighbors criterion.Applied Psychological Measurement,24,257-265.
6. Cover, T. M.,Thomas, J. A.(1991).Elements of information theory.New York:John Wiley & Sons.
7. Drasgow, F.(1989).An evaluation of marginal maximum likelihood estimation for the two-parameter logistic model.Applied psychological measurement,13,77-90.
8. Green, D. R.,Yen, W. M.,Burket, G. R.(1989).Experiences in the application of item response theory in test construction.Applied Measurement in Education,2(4),297-312.
9. Hung, P. H.(1988).Minneapolis, MN,University of Minnesota.
10. Kullback, S.(1959).Information theory and statistics.New York:John Wiley & Sons.
11. Lord, F. M.(1977).A broad-range tailored test of verbal ability.Applied Psychological Measurement,1(1),95-100.
12. Lord, F. M.(1980).Applications of item response theory to practical testing problems.Hillsdale, NJ:Lawrence Erlbaum Associates.
13. McBride, J. R.,Martin, J. T.,D. J. Weiss (Ed.)(1983).New horizons in testing.New York:Academic Press.
14. Mislevy, R. J.,Stocking, M. L.(1989).A consumer's Guide to LOGIST and BILOG.Applied Psychological Measurement,13(1),57-75.
15. Reckase, M. D.(1973).An interactive computer program for tailored testing based on the one-parameter logistic model.Paper presented to the National Conference on the Use of On-Line computers in Psychology
16. Skaggs, G.,Stevenson, J.(1989).A comparison of pseudobayesian and joint maximum likelihood procedures for estimating item parameters in the three-parameter IRT model.Applied psychological measurement,13(4),391-402.
17. Stocking, M. L.(1994).Three practical issues for modern adaptive testing item pools.ERIC Document Reproduction Service No. ED385 551.
18. Stone, C. A.(1992).Recovery of marginal maximum likelihood estimates in the two parameter logistic response model: An evaluation of MULTILOG.Applied Psychological Measurement,16,1-16.
19. Urry V. W. A.(1970).West Lafayette, IN,Purdue University.
20. Weiss, D. J.(1974).Strategies of adaptive ability measurement (Research Report 74-75)Strategies of adaptive ability measurement (Research Report 74-75),Minneapolis, MN:University of Minnesota, Department of Psychology, Psychometric Methods Program.
21. 王淑卿(2005)。碩士論文(碩士論文)。台中縣，私立台中健康暨管理學院資訊工程學系。
22. 王寶墉(1995)。現代測驗理論。台北市:心理。
23. 余民寧(1992)。題目反應的介紹－測驗理論的發展趨勢（二）。研習資訊，9(1)，5-9。
24. 李茂能(2000)。中文電腦化適性測驗系統之應用與評鑑。台北市:文景。
25. 洪碧霞、吳裕益、吳鐵雄、陳英豪(1992)。國科會計畫(NSC 81- 0301-H-024-03)國科會計畫(NSC 81- 0301-H-024-03),未出版
26. 郭伯臣(2006)。國立台中教育大學、私立亞洲大學與階梯數位科技股份有限公司建教合作計畫國立台中教育大學、私立亞洲大學與階梯數位科技股份有限公司建教合作計畫,未出版
27. 陳麗如(1998)。碩士論文(碩士論文)。台北市，國立台灣師範大學資訊教育研究所。
28. 黃建智、程爾觀、劉長萱(2003)。適性測驗之曝光率。2004年科技化測驗與能力指標評量國際研討會論文集，台南市:

1. 王榮照(2014)。模擬研究適合的實驗設計。運動教練科學，33，67-77。