Research Article
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Year 2022, Volume: 5 Issue: 3, 573 - 584, 30.09.2022
https://doi.org/10.31681/jetol.1099580

Abstract

References

  • Bao, Y., Shen, Y., Wang, S., & Bradshaw, L. (2020). Flexible computerized adaptive tests to detect misconceptions and estimate ability simultaneously. Applied Psychological Measurement, 45(1), 3-21. https://doi.org/10.1177/0146621620965730
  • Dooley, K. (2002). Simulation research methods. In J. Baum (Ed.), Companion to organizations (pp. 829-848). Blackwell.
  • Eggen, T. J. H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement, 23(3), 249-261. https://doi.org/10.1177/01466219922031365
  • Eggen, T. J. H. M., & Straetmans, G. J. J. M. (2000). Computerized adaptive testing for classifying examinees into three categories. Educational and Psychological Measurement, 60(5), 713-734. https://doi.org/10.1177/00131640021970862
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologist. Lawrence Erlbaum Associates Publishers.
  • Fan, Z., Wang, C., Chang, H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. https://doi.org/10.3102/1076998611422912
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Finkelman, M. (2008). On using stochastic curtailment to shorten the SPRT in sequential mastery testing. Journal of Educational and Behavioral Statistics, 33(4), 442-463. https://doi.org/10.3102/1076998607308573
  • Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Kluwer Nijhoff Publishing.
  • Harwell, M., Stone, C.A., Hsu, T.C., & Kirisci L. (1996). Monte Carlo studies in item response theory. Applied Psychological Measurement, 20(2), 101-125. https://doi.org/10.1177/014662169602000201
  • Huebner, A. (2012). Item overexposure in computerized classification tests using sequential item selection. Practical Assessment, Research & Evaluation, 17(12), 1-9. https://doi.org/10.7275/nrlc-yv82
  • Huebner, A., & Li, Z. (2012). A stochastic method for balancing item exposure rates in computerized classification tests. Applied Psychological Measurement, 36(3), 181-188. https://doi.org/10.1177/0146621612439932
  • Huo, Y. (2009). Variable-length computerized adaptive testing: adaptation of the a-stratified strategy in item selection with content balancing. Unpublished doctoral dissertation. University of Illinois, Champaign. http://hdl.handle.net/2142/14715
  • Kingsbury, G. G., & Weiss, D. J. (1980). A Comparison of adaptive, sequential and conventional testing strategies for mastery decisions (Research Report 80-4). University of Minnesota, Minneapolis: MN. http://iacat.org/sites/default/files/biblio/ki80-04.pdf
  • Kingsbury, G. G., & Weiss, D.J. (1983). A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure. In D. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing, (pp. 237-254). Academic Press.
  • 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
  • Krabbe, P. F. M. (2017). The Measurement of Health and Health Status: Concepts, Methods and Applications from a Multidisciplinary Perspective. New Developments (Ch.14, ss. 309-331). Academic Press. https://doi.org/10.1016/B978-0-12-801504-9.00014-3
  • 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
  • Leung, C.-K., Chang, H. H., & Hau, K. T. (2002). Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson–Hetter algorithm. Applied Psychological Measurement, 26(4), 376-392. https://doi.org/10.1177/014662102237795
  • Lin, C. (2011). Item selection criteria with practical constraints for computerized classification testing. Applied Psychological Measurement 71(1), 20-36. https://doi.org/10.1177/0013164410387336
  • Lin, C. J., & Spray, J. (2000). Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT (Research Report 2000-8). Iowa city, IA: ACT Research Report Series. https://eric.ed.gov/?id=ED445066
  • Spray, J. A. & Reckase, M. D. (1994). The Selection of Test Items for Decision Making with a Computer Adaptive Test. The Annual Meeting of the National Council on Measurement in Education. NewOrleans, LA, 5-7 April 1994. https://eric.ed.gov/?id=ED372078
  • Spray, J. A., & Reckase, M. D. (1996). Comparison of SPRT and sequential bayes procedures for classifying examinees into two categories using a computerized test. Journal of Educational and Behavioral Statistics, 21(4), 405-414. https://doi.org/10.3102/10769986021004405
  • Sympson, J. B., & Hetter, R. D. (1985, October). Controlling item exposure rates in computerized adaptive testing. In Proceedings of the 27th annual meeting of the Military Testing Association (pp. 937-977). San Diego, CA: Navy Personnel Research and Development Center. http://www.iacat.org/content/controlling-item-exposure-rates-computerized-adaptive-testing
  • Thompson, N. A. (2007). A practitioner’s guide for variable-length computerized classification testing. Practical Assessment, Research & Evaluation, 12(1), 1-13. http://www.iacat.org/sites/default/files/biblio/th07-01.pdf
  • Thompson, N. A. (2009). Item selection in computerized classification testing. Educational and Psychological Measurement, 69(5), 778-793. https://doi.org/10.1177/0013164408324460
  • Thompson, N. A. (2011). Termination criteria for computerized classification testing. Practical Assessment, Research & Evaluation, 16(4), 1-7. https://doi.org/10.7275/wq8m-zk25
  • Thompson, N. A., & Ro, S. (2007). Computerized classification testing with composite hypotheses. In D. J. Weiss (Ed.). Proceedings of the 2007 GMAC conference on computerized adaptive testing. http://www.iacat.org/sites/default/files/biblio/cat07nthompson.pdf
  • Van der Linden, W. J., & Veldkamp, B. P. (2004). Constraining item exposure in computerized adaptive testing with shadow tests. Journal of Educational and Behavioral Statistics, 29(3), 273-291. https://doi.org/10.3102/10769986029003273
  • Wainer, H., & Thissen, D. (1987). Estimating ability with the wrong model. Journal of Educational Statistics, 12(4), 339–368. https://doi.org/10.2307/1165054
  • Wang, S., & Wang, T. (2001). Precision of warm’s weighted likelihood estimates for a polytomous model in computerized adaptive testing. Applied Psychological Measurement, 25(4), 317–331. https://doi.org/10.1177/01466210122032163
  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427-450. https://doi.org/10.1007/BF02294627
  • Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361-375. https://doi.org/10.1111/j.1745-3984.1984.tb01040.x

The effect of item pool and selection algorithms on computerized classification testing (CCT) performance

Year 2022, Volume: 5 Issue: 3, 573 - 584, 30.09.2022
https://doi.org/10.31681/jetol.1099580

Abstract

The purpose of this research was to evaluate the effect of item pool and selection algorithms on computerized classification testing (CCT) performance in terms of some classification evaluation metrics. For this purpose, 1000 examinees’ response patterns using the R package were generated and eight item pools with 150, 300, 450, and 600 items having different distributions were formed. A total of 100 iterations were performed for each research condition. The results indicated that average classification accuracy (ACA) was partially lower, but average test length (ATL) was higher in item pools having a broad distribution. It was determined that the observed differences were more apparent in the item pool with 150 items, and that item selection methods gave similar results in terms of ACA and ATL. The Sympson-Hetter method indicated advantages in terms of test efficiency, while the item eligibility method offered an improvement in terms of item exposure control. The modified multinomial model, on the other hand, was more effective in terms of content balancing.

References

  • Bao, Y., Shen, Y., Wang, S., & Bradshaw, L. (2020). Flexible computerized adaptive tests to detect misconceptions and estimate ability simultaneously. Applied Psychological Measurement, 45(1), 3-21. https://doi.org/10.1177/0146621620965730
  • Dooley, K. (2002). Simulation research methods. In J. Baum (Ed.), Companion to organizations (pp. 829-848). Blackwell.
  • Eggen, T. J. H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement, 23(3), 249-261. https://doi.org/10.1177/01466219922031365
  • Eggen, T. J. H. M., & Straetmans, G. J. J. M. (2000). Computerized adaptive testing for classifying examinees into three categories. Educational and Psychological Measurement, 60(5), 713-734. https://doi.org/10.1177/00131640021970862
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologist. Lawrence Erlbaum Associates Publishers.
  • Fan, Z., Wang, C., Chang, H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. https://doi.org/10.3102/1076998611422912
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
  • Finkelman, M. (2008). On using stochastic curtailment to shorten the SPRT in sequential mastery testing. Journal of Educational and Behavioral Statistics, 33(4), 442-463. https://doi.org/10.3102/1076998607308573
  • Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Kluwer Nijhoff Publishing.
  • Harwell, M., Stone, C.A., Hsu, T.C., & Kirisci L. (1996). Monte Carlo studies in item response theory. Applied Psychological Measurement, 20(2), 101-125. https://doi.org/10.1177/014662169602000201
  • Huebner, A. (2012). Item overexposure in computerized classification tests using sequential item selection. Practical Assessment, Research & Evaluation, 17(12), 1-9. https://doi.org/10.7275/nrlc-yv82
  • Huebner, A., & Li, Z. (2012). A stochastic method for balancing item exposure rates in computerized classification tests. Applied Psychological Measurement, 36(3), 181-188. https://doi.org/10.1177/0146621612439932
  • Huo, Y. (2009). Variable-length computerized adaptive testing: adaptation of the a-stratified strategy in item selection with content balancing. Unpublished doctoral dissertation. University of Illinois, Champaign. http://hdl.handle.net/2142/14715
  • Kingsbury, G. G., & Weiss, D. J. (1980). A Comparison of adaptive, sequential and conventional testing strategies for mastery decisions (Research Report 80-4). University of Minnesota, Minneapolis: MN. http://iacat.org/sites/default/files/biblio/ki80-04.pdf
  • Kingsbury, G. G., & Weiss, D.J. (1983). A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure. In D. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing, (pp. 237-254). Academic Press.
  • 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
  • Krabbe, P. F. M. (2017). The Measurement of Health and Health Status: Concepts, Methods and Applications from a Multidisciplinary Perspective. New Developments (Ch.14, ss. 309-331). Academic Press. https://doi.org/10.1016/B978-0-12-801504-9.00014-3
  • 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
  • Leung, C.-K., Chang, H. H., & Hau, K. T. (2002). Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson–Hetter algorithm. Applied Psychological Measurement, 26(4), 376-392. https://doi.org/10.1177/014662102237795
  • Lin, C. (2011). Item selection criteria with practical constraints for computerized classification testing. Applied Psychological Measurement 71(1), 20-36. https://doi.org/10.1177/0013164410387336
  • Lin, C. J., & Spray, J. (2000). Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT (Research Report 2000-8). Iowa city, IA: ACT Research Report Series. https://eric.ed.gov/?id=ED445066
  • Spray, J. A. & Reckase, M. D. (1994). The Selection of Test Items for Decision Making with a Computer Adaptive Test. The Annual Meeting of the National Council on Measurement in Education. NewOrleans, LA, 5-7 April 1994. https://eric.ed.gov/?id=ED372078
  • Spray, J. A., & Reckase, M. D. (1996). Comparison of SPRT and sequential bayes procedures for classifying examinees into two categories using a computerized test. Journal of Educational and Behavioral Statistics, 21(4), 405-414. https://doi.org/10.3102/10769986021004405
  • Sympson, J. B., & Hetter, R. D. (1985, October). Controlling item exposure rates in computerized adaptive testing. In Proceedings of the 27th annual meeting of the Military Testing Association (pp. 937-977). San Diego, CA: Navy Personnel Research and Development Center. http://www.iacat.org/content/controlling-item-exposure-rates-computerized-adaptive-testing
  • Thompson, N. A. (2007). A practitioner’s guide for variable-length computerized classification testing. Practical Assessment, Research & Evaluation, 12(1), 1-13. http://www.iacat.org/sites/default/files/biblio/th07-01.pdf
  • Thompson, N. A. (2009). Item selection in computerized classification testing. Educational and Psychological Measurement, 69(5), 778-793. https://doi.org/10.1177/0013164408324460
  • Thompson, N. A. (2011). Termination criteria for computerized classification testing. Practical Assessment, Research & Evaluation, 16(4), 1-7. https://doi.org/10.7275/wq8m-zk25
  • Thompson, N. A., & Ro, S. (2007). Computerized classification testing with composite hypotheses. In D. J. Weiss (Ed.). Proceedings of the 2007 GMAC conference on computerized adaptive testing. http://www.iacat.org/sites/default/files/biblio/cat07nthompson.pdf
  • Van der Linden, W. J., & Veldkamp, B. P. (2004). Constraining item exposure in computerized adaptive testing with shadow tests. Journal of Educational and Behavioral Statistics, 29(3), 273-291. https://doi.org/10.3102/10769986029003273
  • Wainer, H., & Thissen, D. (1987). Estimating ability with the wrong model. Journal of Educational Statistics, 12(4), 339–368. https://doi.org/10.2307/1165054
  • Wang, S., & Wang, T. (2001). Precision of warm’s weighted likelihood estimates for a polytomous model in computerized adaptive testing. Applied Psychological Measurement, 25(4), 317–331. https://doi.org/10.1177/01466210122032163
  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427-450. https://doi.org/10.1007/BF02294627
  • Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361-375. https://doi.org/10.1111/j.1745-3984.1984.tb01040.x
There are 33 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Seda Demir 0000-0003-4230-5593

Publication Date September 30, 2022
Published in Issue Year 2022 Volume: 5 Issue: 3

Cite

APA Demir, S. (2022). The effect of item pool and selection algorithms on computerized classification testing (CCT) performance. Journal of Educational Technology and Online Learning, 5(3), 573-584. https://doi.org/10.31681/jetol.1099580


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