建議

本節就本研究未盡完備之處，提出一些研究建議，供後研究者參考。

壹、平衡設計與不平衡設計相當的多元，本研究以試題對應到的概念數相同與否做 為平衡與不平衡的標準，且不平衡的矩陣設計，有著較佳的估計效果，後續研 究者，可以更大的差距來做不平衡的設計，或定義不同的平衡與不平衡的標準 來探討。

貳、本研究試題長度設定為 20 題、30 題，且以 30 題的估計成效較佳，後續研究者 可以不同的試題長度來探討。

參、本研究之人數樣本數設定僅設定成 100 人、500 人、1000 人，且在概念數 10 個時，因其組合已達 1024 種情形，致估計效果不佳，後續研究者可以 2000 人 或 2000 人以上的樣本數來探討當概念數較大時模式的估計效果。

肆、高階層能力的分佈，本研究以常態分佈 N(0,1)來取得，後續研究者可以不同的 分佈情形來做探討。

伍、試題參數本研究僅設定 s=g=0.1，及 s=g=0.25，並未討論其他數值或s ≠g 的情 形，後續研究者可以不同的設定來討論 。

陸、認知屬性的數量，研究者可以設定不同的數量來探討。

柒、受試者認知屬性的分佈，本研究僅設計標準常態分佈及均勻分佈，後續研究者 可以常態、偏峰、雙峰…等，不同分佈來探討。

參考文獻 參考文獻 參考文獻 參考文獻

中文部份

甘媛源與余嘉元(2009)。心理測量理論的新進展：潛在分類模型。中國考試，2009(3)，

3-8。

余民寧(2009)。試題反應理論(IRT)及其應用。臺北市：心理出版社。

涂金堂(2003)。認知診斷評量的探究。臺南師範學院學報，37(2):67-97。

洪祥堯(2009)。資訊科技融入國小六年級圓形圖單元教學與評量之行動研究。亞 洲大學資訊工程學系碩士論文，未出版，臺中縣。

英文部份

Bock, R. D. & Aitkin, M. (1981) Marginal maximum likelihood estimation of item parameters：Application of an EM algorithm. Psychometrika, 46, 443-459.

Cheng, Y., & Chang, H. (2009). When cognitive diagnosis meets computerized adaptive testing: CD-CAT. Psychometrika, 74(4), 619–632.

de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika. 69(3),333-353.

de la Torre, J., & Douglas, J. (2008). Model evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data.

Psychometrika,73(4), 595-624.

de la Torre, J., & Liu, Y. (2008, March). A cognitive diagnosis model for continuous response. Paper presented at the meeting of the National Council on

Measurement in Education, New York, NY.

de la Torre, J. (2009a). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34, 115-130.

de la Torre, J. (2009b). A cognitive diagnosis model for cognitively-based

multiple-choice options. Applied Psychological Measurement, 33, 163-183.

de la Torre, J.,& Lee (2010). A note on the invariance of DINA model parameters.

Journal of Measurements, 47, 115-127.

Doornik, J. A. (2003). Object-oriented matrix programming using Ox (Version 3.1).

[Computer software]. London, England: Timberlake Consultants Press.

Doignon, J.P., & Falmagne, J.C. (1999). Knowledge spaces. New York: Springer.

Educational Testing Service (2004). Arpeggio: Release 1.1[Computer software].

Princeton, NJ: Author.

Fischer, G. H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 37,59-374.

Finkelman, M., Kim, W., & Roussos, L. (2009). Automated test assembly for

cognitive diagnostic models using a genetic algorithm. Journal of Educational Measurement, 46 (3), 273-292.

Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transcationa on Pattern Analysis and Machine Intelligence, 6, 721-741.

Gierl, M., Cui, Y., & Zhou, J. (2009). Reliability and attribute-based scoring in cognitive diagnostic assessment. Journal of Educational Measurement, 46 (3), 293-313.

Henson, R. A., & Douglas, J. (2005). Test construction for cognitive diagnosis.

Applied Psychological Measurement, 29 (4), 262-277.

Hartz, S. (2002). A Bayesian framework for the Unified Model for assessing cognitive abilities: blending theory with practice. Unpublished doctoral thesis, University of Illinois at Urbana-Champain.

Henson, R., Templin J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210.

Huebner, (2010).Cognitive Diagnostic Computer Adaptive Assessments . Journal of Educational Measurement, 46 (3),293-313.

Huebner, Alan, (2010). An Overview of Recent Developments in Cognitive

Diagnostic Computer Adaptive Assessments. Practical Assessment, Research &

Evaluation, 15(3), January 2010, form http://pareonline.net/getvn.asp?v=15

&n=3.

Junker, B., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied

Psychological Measurement, 25(3), 258-272.

Leighton, J. P., Gierl, M. J., & Hunka, S. M. (2004). The attribute hierarchy method for cognitive assessment: a variation on Tatsuoka’s rule space approach.

Journal of Educational Measurement, 41(3), 205–237.

Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64(2), 187-212.

McGlohen, M., & Chang, H. (2008). Combining computer adaptive testing technology with cognitively diagnostic assessment. Behavior Research Methods, 40 (3), 808-821.

Muthén, L.K., & Muthén, B.O. (1998-2006). M-plus user’s guide(4th ed.). Los Angeles: Muthén, L.K., & Muthén.

Patz, R. J., & Junker, B. W. (1999). A straightforward approach to Markov Chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24(2), 146-178.

Rupp, A., & Templin, J. (2008). The effects of q-matrix misspecification on parameter Estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68(1), 78-96.

Sheehan, K, M.(1997). A tree-based approach to proficiency scaling and diagnostic assessment . Journal of Educational Measurement, 34, 333-352.

Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconception based on item response theory. Journal of Educational Measurement, 20, 345-354.

Tatsuoka, K. K. (1985).A probabilistic model for diagnosing misconceptions by the pattern classification approach. Journal of Educational Statistics, 10, 55-73.

Templin, J. L., Henson, R. A., L. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287–305 Templin, J. L., Henson, R. A., Templin, S. E., & Roussos, L. (2008). Robustness of

Hierarchical Modeling of Skill Association in Cognitive Diagnosis Models.

Applied Psychological Measurement, 32(7), 559–574.

Tierney, L. (1994). Exploring posterior distributions with Markov Chains. Annals of Statistics, 22, 1701-1762.

von Davier, M. (2005). A General diagnostic model applied to language testing data.

ETS Research Report. Princeton, New Jersey: ETS.

Xu, X., Chang, H., & Douglas, J. (2003). A simulation study to compare CAT

strategies for cognitive diagnosis. Paper presented at the annual meeting of the American Educational Research Association, Chicago.

Xu, X. & von Davier, M. (2008). Linking for the general diagnostic model. ETS Research Report. Princeton, New Jersey: ETS.