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研究生:洪裕堂
研究生(外文):HONG, YUK-TARNG
論文名稱:同時使用作答反應及作答反應時間估計概念之認知診斷模型探究
論文名稱(外文):COGNITIVE DIAGNOSTIC MODELS FOR RESPONSES AND RESPONSE TIMES
指導教授:郭伯臣郭伯臣引用關係
指導教授(外文):KUO, BOR-CHEN
口試委員:張郁雯施淑娟曾建銘楊智為郭伯臣
口試委員(外文):CHANG, YU-WENSHIH, SHU-CHUANCHENG, CHIEN-MINGYANG, JHIH-WEIKUO, BOR-CHEN
口試日期:2022-04-24
學位類別:博士
校院名稱:國立臺中教育大學
系所名稱:教育資訊與測驗統計研究所
學門:教育學門
學類:教育測驗評量學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:80
中文關鍵詞:認知診斷模型作答反應作答反應時間反應速度時間強度
外文關鍵詞:cognitive diagnosis modelsresponseresponse timetime intensityspeed
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  • 下載下載:39
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近年來認知診斷模型越來越受重視。大部份的認知診斷模型是以受試者的作答反應來診斷受試者解題概念的有無,部份為應用受試者作答反應時間來診斷受試者解題概念的認知診斷模型,也有同時使用作答反應與作答反應時間來診斷受試者概念的認知診斷模型。但因為作答反應時間被使用的頻率越益頻繁,本研究擬提出同時考慮受試者能力、反應速度與試題作答反應參數、時間強度來估計概念之認知診斷模型。以理論探討本研究所提出之模型與其它認知診斷模型的關係,並提出參數估計的方法,透過模擬研究探索不同變數對於模型參數的影響,以及將反應速度與時間強度加入認知反應模型的成效。研究結果顯示:
一、本研究所提出之新認知診斷模型的參數回復與文獻中相關的認知診斷模型相比表現類似,代表估計結果可信。
二、同時使用作答反應時間與作答反應的認知診斷模型比只使用作答反應或只使用作答反應時間的認知診斷模型在正確分類率表現得更好。
三、本研究所提出之新認知診斷模型在低品質試題時的表現與文獻中相關的認知診斷模型相比明顯更為優異。

Cognitive diagnosis models have drawn much attention in recent years. Most applications of CDMs (Cognitive Diagnosis Models, CDMs) based on item response theory provide diagnosis information about student cognitive skills. As item response times are widely used in CDMs, this study aims to develop CDMs which contain various sourses: response and response time, and consider various parameter: slipping、guessing、time intensity、ability and speed. To investigate the difference between the theory of the CDMs applied in this study and others, parameter estimate is used to discuss how variables affect parameters in the utility of simulation study and the effect of the conjunction of response and response time in CDMs in this study. The results show that:
1.The parameter recovery of the new cognitive diagnosis model proposed in this study was similar to the related cognitive diagnosis models.
2.The cognitive diagnosis models used both response and response times has better performance at correct classification rates than cognitive diagnosis models used response or response times respectively.
3.The new cognitive diagnosis model proposed in this study on low-quality items has better performance significantly than the related cognitive diagnosis models.

目 錄 III
表目錄 IV
圖目錄 V
第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 待答問題 4
第四節 名詞解釋 4
第二章 文獻探討 5
第一節 使用作答反應的CDMs 6
第二節 使用作答反應時間的CDMs 8
第三節 同時使用作答反應與作答反應時間的CDMs 9
第三章 研究方法 15
第一節 JCDD模型 15
第二節 JCDSD模型 17
第三節 MCMC估計方法 19
第四節 實驗設計 24
第四章 模擬資料結果與分析 33
第一節 研究一之研究結果 33
第二節 研究二之研究結果 41
第三節 研究三之研究結果 42
第四節 研究四之研究結果 49
第五節 綜合探討 56
第五章 結論與建議 59
參考文獻 61
附錄 63
附錄一 JCDSD模型的JAGS程式碼 67
附錄二 JCDD模型的JAGS程式碼 69

Bolsinova, M., & Maris, G. (2016). A test for conditional independence between response time and accuracy. British Journal of Mathematical and Statistical Psychology, 69, 62–67.
DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: The DINA model, classification, latent class sizes, and the Q-matrix. Applied Psychological Measurement, 35, 8–26.
de la Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34, 115–130.
de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333–353.
Fan, Z., Wang, C., Chang, H.-H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37, 655–670.
Ferrando, P. J., & Lorenzo-Seva, U. (2007). An item response theory model for incorporating response time data in binary personality items. Applied Psychological Measurement, 31, 525–543.
Fox, J.-P., & Marianti, S. (2017). Person-fit statistics for joint models for accuracy and speed. Journal of Educational Measurement, 54, 243–262.
Huebner, A. (2010). An Overview of Recent Developments in Cognitive Diagnosis Computer Adaptive Assessments. Practical Assessment, Research & Evaluation, 15(3), 1-7.
Klein Entink, R. H., Fox, J.-P., & van der Linden, W. J. (2009). A multivariate multilevel approach to the modeling of accuracy and speed of test takers. Psychometrika, 74, 21–48.
Meng, X.-B., Tao, J., & Chang, H.-H. (2015). A conditional joint modeling approach for locally dependent item responses and response times. Journal of Educational Measurement, 52, 1–27.
Meng, X.-B., Tao, J., & Shi, N.-Z. (2014). An item response model for Likert-type data that incorporates response time in personality measurements. Journal of Statistical Computation and Simulation, 84, 1–21.
Minchen, N. D. (2016). Continuous response in cognitive diagnosis models:response time modeling, computerized adaptive testing, and q-matrix validation (Unpublished doctoral dissertation). Rutgers, The State University of New Jersey, New Jersey, USA.
Minchen, N. D., de la Torre, J., & Liu, Y. (2017). A cognitive diagnosis model for continuous response. Journal of Educational and Behavioral Statistics, 20, 1–27
Mislevy, R. J. (1993). Foundations of a new test theory. In N. Frederiksen, R. J. Mislevy, I. I. Bejar. (Eds.), Test theory for a new generation of tests. Hillsdale, NJ: Lawrence Erlbaum Associates.
Nichols, P. D. (1994). A framework for developing cognitively diagnosis assessment. Review of Educational Research, 64, 575–603.
Noel, Y., & Dauvier, B. (2007). A beta item response model for continuous bounded responses. Applied Psychological Measurement, 31, 47–73.
Roskam, E. E. (1987). Toward a psychometric theory of intelligence. In E. E. Roskam & R. Suck (Eds.), Progress in mathematical psychology, 1 (pp. 151–174). New York, NY: Elsevier Science.
Schloss, P. J., & Sedlak, R. A. (1986). Instructional methods for students with learning and behavior problems. Boston: Allyn & Bacon.
Sie, H., Finkelman, M. D., Riley, B., & Smits, N. (2015). Utilizing response times in computerized classification testing. Applied Psychological Measurement, 39, 389–405.
Su, Y.-S., & Yajima, M. (2015). R2jags: Using Rto run ‘JAGS’. R package version 0.5-7. Retrieved from http://CRAN.R-project.org/package=R2jags
Tatsuoka, K. K. (1985). A probabilistic model for diagnosing misconceptions by the pattern classification approach. Journal of Educational Statistics, 10(1), 55–73.
Thissen, D., Steinberg, L., Pyszczynski, T., & Greenberg, J. (1983). An item response theory for personality and attitude scales: Item analysis using restricted factor analysis. Applied Psychological Measurement, 7, 211–226.
van der Linden, W. J. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31, 181–204.
van der Linden, W. J. (2007). A hierarchical framework for modeling speed and accuracy on test items. Psychometrika, 72, 287–308.
van der Linden, W. J. (2008). Using response times for item selection in adaptive testing. Journal of Educational and Behavioral Statistics, 33, 5–20.
van der Linden, W. J. (2009). Conceptual issues in response-time modeling. Journal of Educational Measurement, 46, 247–272.
van der Linden, W. J., Breithaupt, K., Chuah, S. C., & Zhang, Y. (2007). Detecting differential speededness in multistage testing. Journal of Educational Measurement, 44, 117–130.
van der Linden, W. J., & Guo, F. (2008). Bayesian procedures for identifying aberrant response-time patterns in adaptive testing. Psychometrika, 73, 365–384.
van der Linden, W. J., Scrams, D. J., & Schnipke, D. L. (1999). Using response-time constraints to control for speededness in computerized adaptive testing. Applied Psychological Measurement, 23, 195–210.
van der Linden, W. J., & van Krimpen-Stoop, E. M. L. A. (2003). Using response times to detect aberrant response patterns in computerized adaptive testing. Psychometrika, 68, 251–265.
van der Linden, W. J., & Xiong, X. (2013). Speededness in adaptive testing. Journal of Educational and Behavioral Statistics, 38, 418–438.
van der Maas, H. L., & Jansen, B. R. J. (2003).What response times tell of children’s behavior on the balance scale task. Journal of Experimental Child Psychology, 85, 141–177.
van der Maas, H. L., & Wagenmakers, E. J. (2005). A psychometric analysis of chess expertise. The American Journal of Psychology, 118, 29–60.
Wang, C., & Xu, G. (2015). A mixture hierarchical model for response times and response accuracy. British Journal of Mathematical and Statistical Psychology, 68, 456–477.
Wang, T., & Hanson, B. A (2005). Development and calibration of an item response model that incorporates response time. Applied Psychological Measurement, 29, 323–339.
Zhan, P., Jiao, H., & Liao, D. (2018). Cognitive diagnosis modelling incorporating item response times. British Journal of Mathematical and Statistical Psychology, 71, 262–286.

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