A Systematic Review on Computerized Adaptive Testing

Year 2025, Volume: 27 Issue: 1, 137 - 150, 31.03.2025

Hümeyra Demir , Selahattin Gelbal

https://doi.org/10.17556/erziefd.1577880

Abstract

The aim of this research is to systematically review studies related to Computerized Adaptive Testing (CAT). Following systematic review guidelines, 110 articles were evaluated to seek answers to the established questions. These articles were analyzed based on their objectives, results, and recommendations, leading to a general conclusion. The compiled articles highlighted that innovative methods for CAT emerged as the most researched area. Within these innovative methods, the most studied topics were item selection algorithms and cognitive diagnosis computerized adaptive testing (CD-CAT). The results indicate that CAT enhances the accuracy and efficiency of tests through newly developed methods. It has been determined that CAT facilitates the provision of short and effective tests tailored to students' knowledge levels, ensures applicability across various disciplines, and offers the opportunity to reach large audiences through remote education platforms. The study concludes that to promote wider acceptance of CAT and increase its effectiveness, there is a need for the development of software tools and research focused on user attitudes. This study aims to identify potential future development areas for CAT, thereby enhancing the effectiveness of personalized assessment systems in education.

Keywords

Computerized adaptive testing, systematic review, innovative methods for CAT, research trends in CAT.

Bireyselleştirilmiş Bilgisayarlı Testler Üzerine Sistematik Bir Derleme

Abstract

Bu araştırmanın amacı, Bireyselleştirilmiş Bilgisayarlı Testler (BBT) ile ilgili yapılmış çalışmaları sistematik bir derleme yöntemiyle incelemektir. Sistematik derleme kurallarına uygun olarak, 110 makale detaylı bir şekilde değerlendirilmiştir. Bu makaleler, her birinin amacı, sonuçları ve önerileri doğrultusunda analiz edilerek genel bir çıkarıma ulaşılmıştır. Analiz sonucunda, BBT’ler için yenilikçi yöntemlerin en fazla araştırılan alan olduğu tespit edilmiştir. Özellikle, madde seçim algoritmaları ve bilişsel tanı tabanlı BBT’ler gibi konular, bu yenilikçi yöntemler arasında öne çıkmaktadır. Elde edilen sonuçlar, BBT’lerin yeni geliştirilen yöntemlerle testlerin doğruluğunu ve verimliliğini artırdığını göstermektedir. BBT’lerin, öğrencilerin bilgi seviyesine uygun, kısa ve etkili testler sunma konusundaki avantajları, farklı disiplinlerde uygulanabilirliği sağlama potansiyeli ve uzaktan eğitim platformları üzerinden geniş kitlelere ulaşma imkânı sunduğu belirlenmiştir. Ayrıca, BBT’lerin daha yaygın kabul edilmesi ve etkinliğinin artırılması için yazılım araçlarının geliştirilmesi ve kullanıcı tutumlarına yönelik daha fazla araştırma yapılması gerektiği sonucuna ulaşılmıştır. Bu çalışma, BBT’lerin gelecekteki potansiyel gelişim alanlarını belirleyerek, eğitimde kişiselleştirilmiş değerlendirme sistemlerinin etkinliğini artırmayı hedeflemektedir.

Keywords

Bireyselleştirilmiş bilgisayarlı testler, sistematik derleme, BBT’ler için yenilikçi yöntemler, BBT’ler için araştırma eğilimleri.

References

• Adams, Z. W., Hulvershorn, L. A., Smoker, M. P., Marriott, B. R., Aalsma, M. C., & Gibbons, R. D. (2024). Initial validation of a computerized adaptive test for substance use disorder identification in adolescents. Substance Use & Misuse, 59(6), 867–873. https://doi.org/10.1080/10826084.2024.2305801
• Albano, A. D., Cai, L., Lease, E. M., & McConnell, S. R. (2019). Computerized adaptive testing in early education: Exploring the impact of item position effects on ability estimation. Journal of Educational Measurement, 56(2), 437–451. https://doi.org/10.1111/jedm.12215
• Anselmi, P., Robusto, E., & Cristante, F. (2023). Enhancing computerized adaptive testing with batteries of unidimensional tests. Applied Psychological Measurement, 47(3), 167–182. https://doi.org/10.1177/01466216231165301
• Aşiret, S., & Sünbül, S. Ö. (2024). Investigating the performance of item selection algorithms in cognitive diagnosis computerized adaptive testing. Journal of Measurement and Evaluation in Education and Psychology, 15(2), 148–165. https://doi.org/10.21031/epod.1456094
• Ayanwale, M. A., & Ndlovu, M. (2022). Transition from computer-based testing of national benchmark tests to adaptive testing: Robust application of fourth industrial revolution tools. Cypriot Journal of Educational Sciences, 17(9), 3327–3343. https://doi.org/10.18844/cjes.v17i9.7124
• Ayanwale, M. A., & Ndlovu, M. (2024). The feasibility of computerized adaptive testing of the national benchmark test: A simulation study. Journal of Pedagogical Research, 8(2), 95–112. https://doi.org/10.33902/JPR.202425210
• Barrett, M. D., Jiang, B., & Feagler, B. E. (2022). A smart authoring system for designing, configuring, and deploying adaptive assessments at scale. International Journal of Artificial Intelligence in Education, 32(1), 28–47. https://doi.org/10.1007/s40593-021-00258-y
• Bengs, D., Kroehne, U., & Brefeld, U. (2021). Simultaneous constrained adaptive item selection for group-based testing. Journal of Educational Measurement, 58(2), 236–261. https://doi.org/10.1111/jedm.12285
• Bock, R. D., Muraki, E., & Pfeiffenberger, W. (1988). Item pool maintenance in the presence of item parameter drift. Journal of Educational Measurement, 25(4), 275–285. https://doi.org/10.1111/j.1745-3984.1988.tb00308.x
• Braeken, J., & Paap, M. C. (2020). Making fixed-precision between-item multidimensional computerized adaptive tests even shorter by reducing the asymmetry between selection and stopping rules. Applied Psychological Measurement, 44(7–8), 531–547. https://doi.org/10.1177/0146621620932666
• Chang, H. H., & Ying, Z. (2009). Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests. Annals of Statistics, 37(3), 1466–1488. https://doi.org/10.1214/08-AOS614
• Chang, Y. P., Chiu, C. Y., & Tsai, R. C. (2019). Nonparametric CAT for CD in educational settings with small samples. Applied Psychological Measurement, 43(7), 543–561. https://doi.org/10.1177/0146621618813113
• Chao, H. Y., & Chen, J. H. (2023). Controlling the minimum item exposure rate in computerized adaptive testing: A two-stage Sympson–Hetter procedure. Applied Psychological Measurement, 47(7–8), 460–477. https://doi.org/10.1177/01466216231209756
• Chen, C. W., & Liu, C. W. (2023). Online Parameter Estimation for Student Evaluation of Teaching. Applied Psychological Measurement, 47(4), 291-311. https://doi.org/10.1177/01466216231165314
• Chen, C. W., Wang, W. C., Chiu, M. M., & Ro, S. (2020). Item selection and exposure control methods for computerized adaptive testing with multidimensional ranking items. Journal of Educational Measurement, 57(2), 343–369. https://doi.org/10.1111/jedm.12252
• Chen, J. H., & Chao, H. Y. (2024). Utilizing real-time Test Data to solve attenuation paradox in computerized adaptive testing to enhance optimal design. Journal of Educational and Behavioral Statistics, 49(4), 630-657. https://doi.org/10.3102/10769986231197666
• Chen, J. H., Chao, H. Y., & Chen, S. Y. (2020). A dynamic stratification method for improving trait estimation in computerized adaptive testing under item exposure control. Applied Psychological Measurement, 44(3), 182–196. https://doi.org/10.1177/0146621619843820
• Chen, S. Y., Ankenmann, R. D., & Spray, J. A. (2003). The relationship between item exposure and test overlap in computerized adaptive testing. Journal of Educational Measurement, 40(2), 129–145. https://doi.org/10.1111/j.1745-3984.2003.tb01100.x
• Cooperman, A. W., Weiss, D. J., & Wang, C. (2022). Robustness of adaptive measurement of change to item parameter estimation error. Educational and Psychological Measurement, 82(4), 643–677. https://doi.org/10.1177/00131644211033902
• Cui, Z. (2020). A seed usage issue on using catR for simulation and the solution. Applied Psychological Measurement, 44(5), 409–412. https://doi.org/10.1177/0146621620920934
• Cui, Z. (2022). On measuring adaptivity of an adaptive test. Measurement: Interdisciplinary Research and Perspectives, 20(1), 21–33. https://doi.org/10.1080/15366367.2021.1922232
• Davison, M. L., Weiss, D. J., DeWeese, J. N., Ersan, O., Biancarosa, G., & Kennedy, P. C. (2023). A diagnostic tree model for adaptive assessment of complex cognitive processes using multidimensional response options. Journal of Educational and Behavioral Statistics, 48(6), 914–941. https://doi.org/10.3102/10769986231158301
• Dirven, L., Petersen, M. A., Aaronson, N. K., Chie, W. C., Conroy, T., Costantini, A., Hammerlid, E., Velikova, G., Verdonck-de Leeuw, I. M., Young, T., & Groenvold, M. (2021). Development and psychometric evaluation of an item bank for computerized adaptive testing of the EORTC insomnia dimension in cancer patients (EORTC CAT-SL). Applied Research in Quality of Life, 16, 827–844. https://doi.org/10.1007/s11482-019-09799-w
• Ebenbeck, N., & Gebhardt, M. (2022). Simulating computerized adaptive testing in special education based on inclusive progress monitoring data. Frontiers in Education, 7, Article 945733. https://doi.org/10.3389/feduc.2022.945733
• Ebenbeck, N., & Gebhardt, M. (2024). Differential performance of computerized adaptive testing in students with and without disabilities – A simulation study. Journal of Special Education Technology, 39(4), 481–490. https://doi.org/10.1177/01626434241232117
• Fernandes, S., Fond, G., Zendjidjian, X., Michel, P., Baumstarck, K., Lancon, C., Berna, F., Schurhoff, F., Aouizerate, B., Henry, C., Etain, B., Samalin, L., Leboyer, M., Llorca, P. M., Coldefy, M., Auquier, P., & Boyer, L. (2019). The Patient-Reported Experience Measure for Improving Quality of Care in Mental Health (PREMIUM) project in France: Study protocol for the development and implementation strategy. Patient Preference and Adherence, 13, 165–177. https://doi.org/10.2147/PPA.S172100
• Frey, A., König, C., & Fink, A. (2023). A highly adaptive testing design for PISA. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12382
• Gao, X., Wang, D., Cai, Y., & Tu, D. (2020). Cognitive diagnostic computerized adaptive testing for polytomously scored items. Journal of Classification, 37, 709–729. https://doi.org/10.1007/s00357-019-09357-x
• Ghio, F. B., Bruzzone, M., Rojas-Torres, L., & Cupani, M. (2022). Preliminary development of an item bank and an adaptive test in mathematical knowledge for university students. European Journal of Science and Mathematics Education, 10(3), 352–365. https://doi.org/10.30935/scimath/11968
• Gönülateş, E. (2019). Quality of Item Pool (QIP) index: A novel approach to evaluating CAT item pool adequacy. Educational and Psychological Measurement, 79(6), 1133–1155. https://doi.org/10.1177/0013164419842215
• Gu, L., Ling, G., & Qu, Y. (2019). A modified a-stratified method for computerized adaptive testing. ETS Research Report Series, 2019(1), 1–27. https://doi.org/10.1002/ets2.12246 Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Sage.
• Han, K. C. T. (2020). Framework for developing multistage testing with intersectional routing for short-length tests. Applied Psychological Measurement, 44(2), 87–102. https://doi.org/10.1177/0146621619837226
• He, Y., & Qi, Y. (2023). Using response time in multidimensional computerized adaptive testing. Journal of Educational Measurement, 60(4), 697–738. https://doi.org/10.1111/jedm.12373
• He, Y., Chen, P., & Li, Y. (2020). New efficient and practicable adaptive designs for calibrating items online. Applied Psychological Measurement, 44(1), 3–16. https://doi.org/10.1177/0146621618824854
• Hsu, C. L., & Wang, W. C. (2019). Multidimensional computerized adaptive testing using non-compensatory item response theory models. Applied Psychological Measurement, 43(6), 464–480. https://doi.org/10.1177/0146621618800280
• Hsu, C. L., & Wang, W. C. (2022). Reducing the misclassification costs of cognitive diagnosis computerized adaptive testing: Item selection with minimum expected risk. Applied Psychological Measurement, 46(3), 185–199. https://doi.org/10.1177/01466216211066610
• Huang, H. T. D., Hung, S. T. A., Chao, H. Y., Chen, J. H., Lin, T. P., & Shih, C. L. (2022). Developing and validating a computerized adaptive testing system for measuring the English proficiency of Taiwanese EFL university students. Language Assessment Quarterly, 19(2), 162–188. https://doi.org/10.1080/15434303.2021.1984490
• Istiyono, E., Dwandaru, W. S. B., Setiawan, R., & Megawati, I. (2020). Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use. European Journal of Educational Research, 9(1), 91–101. https://doi.org/10.12973/eu-jer.9.1.91
• Jodoin, M. G., Zenisky, A. L., & Hambleton, R. K. (2006). Comparison of the psychometric properties of several computer-based test designs for credentialing exams with multiple purposes. Applied Measurement in Education, 19(3), 203–220. https://doi.org/10.1207/s15324818ame1903_3
• Kang, H. A., Arbet, G., Betts, J., & Muntean, W. (2024). Location-matching adaptive testing for polytomous technology-enhanced items. Applied Psychological Measurement, 48(1–2), 57–76. https://doi.org/10.1177/01466216241227548
• Kang, H. A., Zheng, Y., & Chang, H. H. (2020). Online calibration of a joint model of item responses and response times in computerized adaptive testing. Journal of Educational and Behavioral Statistics, 45(2), 175–208. https://doi.org/10.3102/1076998619879040
• Kaplan, M., & De La Torre, J. (2020). A blocked-CAT procedure for CD-CAT. Applied Psychological Measurement, 44(1), 49–64. https://doi.org/10.1177/0146621619835500
• Kaya, E., O’Grady, S., & Kalender, İ. (2022). IRT-based classification analysis of an English language reading proficiency subtest. Language Testing, 39(4), 541–566. https://doi.org/10.1177/02655322211068847
• Kern, J. L., & Choe, E. (2021). Using a response time–based expected a posteriori estimator to control for differential speededness in computerized adaptive testing. Applied Psychological Measurement, 45(5), 361–385. https://doi.org/10.1177/01466216211014601
• Kim, R. Y., & Yoo, Y. J. (2023). Cognitive diagnostic multistage testing by partitioning hierarchically structured attributes. Journal of Educational Measurement, 60(1), 126–147. https://doi.org/10.1111/jedm.12339
• Kingsbury, G. G., & Zara, A. R. (1989). Procedures for selecting items for computerized adaptive tests. Applied Measurement in Education, 2(4), 359–375. https://doi.org/10.1207/s15324818ame0204_6
• Kisielewska, J., Millin, P., Rice, N., Pego, J. M., Burr, S., Nowakowski, M., & Gale, T. (2024). Medical students’ perceptions of a novel international adaptive progress test. Education and Information Technologies, 29(9), 11323–11338. https://doi.org/10.1007/s10639-023-12269-4
• Komarc, M., Shigeto, A., & Scheier, L. M. (2024). Item response theory and computer adaptive testing of the sexual knowledge scale of the sexual knowledge and attitude test in a college sample. Psychology & Sexuality, 1–18. https://doi.org/10.1080/19419899.2024.2332630
• Lee, C., & Qian, H. (2022). Hybrid threshold-based sequential procedures for detecting compromised items in a computerized adaptive testing licensure exam. Educational and Psychological Measurement, 82(4), 782–810. https://doi.org/10.1177/00131644211023868
• Leroux, A. J., Waid-Ebbs, J. K., Wen, P. S., Helmer, D. A., Graham, D. P., O’Connor, M. K., & Ray, K. (2019). An investigation of exposure control methods with variable-length CAT using the partial credit model. Applied Psychological Measurement, 43(8), 624–638. https://doi.org/10.1177/0146621618824856
• Li, Y., Huang, C., & Liu, J. (2023). Diagnosing primary students’ reading progression: Is cognitive diagnostic computerized adaptive testing the way forward? Journal of Educational and Behavioral Statistics, 48(6), 842–865. https://doi.org/10.3102/10769986231160668
• Lim, H., & Choe, E. M. (2023). Detecting differential item functioning in CAT using IRT residual DIF approach. Journal of Educational Measurement, 60(4), 626–650. https://doi.org/10.1111/jedm.12366
• Lim, H., & Han, K. C. T. (2024). An automated item pool assembly framework for maximizing item utilization for CAT. Educational Measurement: Issues and Practice, 43(1), 39–51. https://doi.org/10.1111/emip.12589
• Lim, H., & Wells, C. S. (2020). irtplay: An R package for online item calibration, scoring, evaluation of model fit, and useful functions for unidimensional IRT. Applied Psychological Measurement, 44(7–8), 563–565. https://doi.org/10.1177/0146621620921247
• Lin, C. J., & Chang, H. H. (2019). Item selection criteria with practical constraints in cognitive diagnostic computerized adaptive testing. Educational and Psychological Measurement, 79(2), 335–357. https://doi.org/10.1177/0013164418790634
• Lin, Y., Brown, A., & Williams, P. (2023). Multidimensional forced-choice CAT with dominance items: An empirical comparison with optimal static testing under different desirability matching. Educational and Psychological Measurement, 83(2), 322–350. https://doi.org/10.1177/00131644221077637
• Linden, W. J., & Glas, G. A. W. (2002). Computerized adaptive testing: Theory and practice. Kluwer Academic Publishers.
• Liu, K., Zhang, L., Tu, D., & Cai, Y. (2022). Developing an item bank of computerized adaptive testing for eating disorders in Chinese university students. SAGE Open, 12(4), 1–13. https://doi.org/10.1177/21582440221141273
• Luo, H., Wang, D., Guo, Z., Cai, Y., & Tu, D. (2022). Combining cognitive diagnostic computerized adaptive testing with multidimensional item response theory. Applied Psychological Measurement, 46(4), 288–302. https://doi.org/10.1177/01466216221084214
• Luo, X., & Wang, X. (2019). Dynamic multistage testing: A highly efficient and regulated adaptive testing method. International Journal of Testing, 19(3), 227–247. https://doi.org/10.1080/15305058.2019.1621871
• Mao, X., Zhang, J., & Xin, T. (2022). The optimal design of bifactor multidimensional computerized adaptive testing with mixed-format items. Applied Psychological Measurement, 46(7), 605–621. https://doi.org/10.1177/01466216221108382
• Mead, A. D., & Drasgow, F. (1993). Equivalence of computerized and paper-and-pencil cognitive ability tests: A meta-analysis. Psychological Bulletin, 114(3), 449–458. https://doi.org/10.1037/0033-2909.114.3.449
• Meijer, R. R., & Nering, M. L. (1999). Computerized adaptive testing: Overview and introduction. Applied Psychological Measurement, 23(3), 187–194. https://doi.org/10.1177/01466219922031310
• Mizumoto, A., Sasao, Y., & Webb, S. A. (2019). Developing and evaluating a computerized adaptive testing version of the Word Part Levels Test. Language Testing, 36(1), 101–123. https://doi.org/10.1177/0265532217725776
• Mohd-Ali, S., Norfarah, N., Ilya-Syazwani, J. I., & Mohd-Erfy, I. (2019). The effect of computerized adaptive testing on reducing anxiety towards math test for polytechnic students. Journal of Technical Education and Training, 11(4), 27–35. https://doi.org/10.30880/jtet.2019.11.04.004
• Montgomery, J. M., & Rossiter, E. L. (2020). So many questions, so little time: Integrating adaptive inventories into public opinion research. Journal of Survey Statistics and Methodology, 8(4), 667–690. https://doi.org/10.1093/jssam/smz027
• Özdemir, B., & Gelbal, S. (2022). Measuring language ability of students with compensatory multidimensional CAT: A post-hoc simulation study. Education and Information Technologies, 27(5), 6273–6294. https://doi.org/10.1007/s10639-021-10853-0
• Öztürk, N. B., & Şahin, M. G. (2019). Effects of item pool characteristics on ability estimate and item pool utilization: A simulation study. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 34(2), 473–486. https://doi.org/10.16986/HUJE.2018042418
• Paap, M. C., Born, S., & Braeken, J. (2019). Measurement efficiency for fixed-precision multidimensional computerized adaptive tests: Comparing health measurement and educational testing using example banks. Applied Psychological Measurement, 43(1), 68–83. https://doi.org/10.1177/0146621618765719
• Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
• Pan, Y., Livne, O., Wollack, J. A., & Sinharay, S. (2023). Item selection algorithm based on collaborative filtering for item exposure control. Educational Measurement: Issues and Practice, 42(4), 6–18. https://doi.org/10.1111/emip.12578
• Pramjeeth, S., & Ramgovind, P. (2023). Students’ and lecturers’ perceptions of computerized adaptive testing as the future of assessing students. Journal of Education (University of KwaZulu-Natal), 93, 120–146. http://dx.doi.org/10.17159/2520-9868/i93a06
• Pranckutė, R. (2021). Web of science (WoS) and scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
• Qiu, X. L., De La Torre, J., Ro, S., & Wang, W. C. (2022). Computerized adaptive testing for ipsative tests with multidimensional pairwise-comparison items: Algorithm development and applications. Applied Psychological Measurement, 46(4), 255–272. https://doi.org/10.1177/01466216221084209
• Raborn, A., & Sarı, H. (2021). Mixed adaptive multistage testing: A new approach. Journal of Measurement and Evaluation in Education and Psychology, 12(4), 358–373. https://doi.org/10.21031/epod.871014
• Reckase, M. D. (1989). Adaptive testing: The evolution of a good idea. Educational Measurement: Issues and Practice, 8(3), 11–15. https://doi.org/10.1111/j.1745-3992.1989.tb00326.x
• Segall, D. O. (2005). Computerized adaptive testing. In Kempf-Leonard (Ed.), The Encyclopedia of Social Measurement (pp. 429–438). Academic Press.
• Seo, D. G., & Choi, J. (2020). Introduction to the LIVECAT web-based computerized adaptive testing platform. Journal of Educational Evaluation for Health Professions, 17, 1–7. https://doi.org/10.3352/jeehp.2020.17.27
• Sorrel, M. A., Abad, F. J., & Nájera, P. (2021). Improving accuracy and usage by correctly selecting: The effects of model selection in cognitive diagnosis computerized adaptive testing. Applied Psychological Measurement, 45(2), 112–129. https://doi.org/10.1177/0146621620977682
• Sulak, S., & Kelecioğlu, H. (2019). Investigation of item selection methods according to test termination rules in CAT applications. Journal of Measurement and Evaluation in Education and Psychology, 10(3), 315–326. https://doi.org/10.21031/epod.530528
• Sun, X., Andersson, B., & Xin, T. (2021). A new method to balance measurement accuracy and attribute coverage in cognitive diagnostic computerized adaptive testing. Applied Psychological Measurement, 45(7–8), 463–476. https://doi.org/10.1177/01466216211040489
• Şenel, S. (2021). Bilgisayar ortamında bireye uyarlanmış testler. Hacettepe Üniversitesi Yayınları.
• Şimşek, A. S., & Tavşancıl, E. (2022). Applicability and efficiency of a polytomous IRT-based computerized adaptive test for measuring psychological traits. Journal of Measurement and Evaluation in Education and Psychology, 13(4), 328–344. https://doi.org/10.21031/epod.1148313
• Tan, Q., Cai, Y., Luo, F., & Tu, D. (2023). Development of a high-accuracy and effective online calibration method in CD-CAT based on Gini index. Journal of Educational and Behavioral Statistics, 48(1), 103–141. https://doi.org/10.3102/10769986221126741
• Tang, X., Zheng, Y., Wu, T., Hau, K. T., & Chang, H. H. (2024). Utilizing response time for item selection in on-the-fly multistage adaptive testing for PISA assessment. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12403
• Tian, C., & Choi, J. (2023). The impact of item model parameter variations on person parameter estimation in computerized adaptive testing with automatically generated items. Applied Psychological Measurement, 47(4), 275–290. https://doi.org/10.1177/01466216231165313
• Tsaousis, I., Sideridis, G. D., & AlGhamdi, H. M. (2021). Evaluating a computerized adaptive testing version of a cognitive ability test using a simulation study. Journal of Psychoeducational Assessment, 39(8), 954–968. https://doi.org/10.1177/07342829211027753
• Tseng, W. T. (2021). The effects of item exposure control on measurement precision of vocabulary size estimates in computerized adaptive testing. English Teaching & Learning, 45(2), 217–236. https://doi.org/10.1007/s42321-020-00068-w
• Van der Linden, W. J., & Glas, C. A. W. (2002). Computerized adaptive testing: Theory and practice. Kluwer Academic.
• Van Wijk, E. V., Donkers, J., De Laat, P. C. J., Meiboom, A. A., Jacobs, B., Ravesloot, J. H., Tio, R. A., Van Der Vleuten, C. P. M., Langers, A. M. J., & Bremers, A. J. A. (2024). Computer adaptive vs. non-adaptive medical progress testing: Feasibility, test performance, and student experiences. Perspectives on Medical Education, 13(1), 406–416. https://doi.org/10.5334/pme.1345
• Wang, C. (2021). On interim cognitive diagnostic computerized adaptive testing in learning context. Applied Psychological Measurement, 45(4), 235–252. https://doi.org/10.1177/0146621621990755
• Wang, C., & Zhu, R. (2024). Detecting uniform differential item functioning for continuous response computerized adaptive testing. Applied Psychological Measurement, 48(1–2), 18–37. https://doi.org/10.1177/01466216241227544
• Wang, S., Xiao, H., & Cohen, A. (2021). Adaptive weight estimation of latent ability: Application to computerized adaptive testing with response revision. Journal of Educational and Behavioral Statistics, 46(5), 560–591. https://doi.org/10.3102/1076998620972800
• Wang, W., Song, L., Wang, T., Gao, P., & Xiong, J. (2020). A note on the relationship of the Shannon entropy procedure and the Jensen–Shannon divergence in cognitive diagnostic computerized adaptive testing. SAGE Open, 10(1), 2158244019899046. https://doi.org/10.1177/2158244019899046
• Wang, Z., Wang, C., & Weiss, D. J. (2022). Termination criteria for grid multiclassification adaptive testing with multidimensional polytomous items. Applied Psychological Measurement, 46(7), 551–570. https://doi.org/10.1177/01466216221108383
• Weiss, D. J., & Şahin, A. (2024). Computerized adaptive testing: From concept to implementation. Guilford Publications.
• Wyse, A. E. (2021). How days between tests impacts alternate forms reliability in computerized adaptive tests. Educational and Psychological Measurement, 81(4), 644–667. https://doi.org/10.1177/0013164420979656
• Wyse, A. E. (2023). Two statistics for measuring the score comparability of computerized adaptive tests. Applied Psychological Measurement, 47(7-8), 513-525. https://doi.org/10.1177/01466216231209749
• Xi, C., Tu, D., & Cai, Y. (2022). Dual-objective item selection methods in computerized adaptive test using the higher-order cognitive diagnostic models. Applied Psychological Measurement, 46(5), 422-438. https://doi.org/10.1177/01466216221089342
• Xiao, J., & Bulut, O. (2022). Item selection with collaborative filtering in on-the-fly multistage adaptive testing. Applied Psychological Measurement, 46(8), 690–704. https://doi.org/10.1177/01466216221124089
• Xu, L., Jiang, Z., Han, Y., Liang, H., & Ouyang, J. (2023). Developing computerized adaptive testing for a national health professionals exam: An attempt from psychometric simulations. Perspectives on Medical Education, 12(1), 462. https://doi.org/10.5334/pme.855
• Yang, J., Chang, H. H., Tao, J., & Shi, N. (2020). Stratified item selection methods in cognitive diagnosis computerized adaptive testing. Applied Psychological Measurement, 44(5), 346–361. https://doi.org/10.1177/0146621619893783
• Yang, L., & Reckase, M. D. (2020). The optimal item pool design in multistage computerized adaptive tests with the p-optimality method. Educational and Psychological Measurement, 80(5), 955-974.
• Yasuda, J. I., Hull, M. M., & Mae, N. (2022). Improving test security and efficiency of computerized adaptive testing for the Force Concept Inventory. Physical Review Physics Education Research, 18(1), 010112. https://doi.org/10.1103/PhysRevPhysEducRes.18.010112
• Yasuda, J. I., Mae, N., Hull, M. M., & Taniguchi, M. A. (2021). Optimizing the length of computerized adaptive testing for the Force Concept Inventory. Physical Review Physics Education Research, 17(1), 010115. https://doi.org/10.1103/PhysRevPhysEducRes.17.010115
• Yıldız, H., Demir, C., Ülkü, S., Giray, G., & Kelecioğlu, H. (2024). Investigation of measurement precision and test length in computerized adaptive tests under different conditions. Journal of Measurement and Evaluation in Education and Psychology-EPOD, 15(1), 5–17. https://doi.org/10.21031/epod.1068572
• Yiğit, H. D., Sorrel, M. A., & De La Torre, J. (2019). Computerized adaptive testing for cognitively based multiple-choice data. Applied Psychological Measurement, 43(5), 388–401. https://doi.org/10.1177/0146621618798665
• Yiğiter, M. S., & Doğan, N. (2023). Computerized multistage testing: Principles, designs and practices with R. Measurement: Interdisciplinary Research and Perspectives, 21(4), 254–277. https://doi.org/10.1080/15366367.2022.2158017
• Yiğiter, M. S., & Doğan, N. (2024). Comparison of different computerized adaptive testing approaches with shadow test under different test lengths and ability estimation method conditions. Journal of Measurement and Evaluation in Education and Psychology-EPOD, 14(4), 396–412. https://doi.org/10.21031/epod.1202599
• Yuan, L., Huang, Y., Li, S., & Chen, P. (2023). Online calibration in multidimensional computerized adaptive testing with polytomously scored items. Journal of Educational Measurement, 60(3), 476–500. https://doi.org/10.1111/jedm.12353
• Yuhana, U. L., Yuniarno, E. M., Rahayu, W., & Pardede, E. (2024). A Context-based question selection model to support the adaptive assessment of learning: A study of online learning assessment in elementary schools in Indonesia. Education and Information Technologies, 29(8), 9517-9540. https://doi.org/10.1007/s10639-023-12184-8
• Yurtçu, M., & Güzeller, C. (2021). Bibliometric analysis of articles on computerized adaptive testing. Participatory Educational Research, 8(4), 426–438. https://doi.org/10.17275/per.21.98.8.4