Comparison of Artificial Intelligence-Assisted Adaptive Testing Methods in Terms of Advantages and Disadvantages
Abstract views: 22
/ 
PDF downloads: 29
Keywords:
Item Response Theory, Adaptive Test, Traditional Test, Knowledge Space Theory, Rasch Model, 2PL Model, 3PL ModelAbstract
Adaptive tests aim to measure individuals' ability levels and knowledge levels on a subject in
the shortest and most accurate way. Unlike classical tests, adaptive tests measure the participant's
knowledge level in a more accurate and short way by creating the test according to the participant's ability
level, instead of asking all the questions to each participant. In this text, adaptive test creation with Item
Response Theory (IRT) and Knowledge Space Theory (KST)), which are important in the field of
measurement, evaluation and teaching are discussed. The model structures of these theorems, their roles in
measurement, evaluation and teaching processes, their purposes of use, and their differences with each
other have been evaluated. In addition, one parameter logistic model (Rasch Model), two parameter logistic
Model (2PL), 3 parameter logistic Model (3PL), which are the most well-known models of Item Response
Theory, are discussed and the advantages and disadvantages of each model compared to the other model
are explained. As a result of relevant research, it is shown that all models offer different approaches and
make different contributions to the fields of measurement, evaluation and teaching.
Downloads
References
Gershon, R. C. (2005). Computer adaptive testing. Journal of applied measurement, 6(1), 109-127.
Weiss, D. J. (1985). Adaptive testing by computer. Journal of consulting and clinical psychology, 53(6), 774.
Van der Linden, W. J., & Glas, C. A. (2000). Computerized adaptive testing: Theory and practice: Springer.
Segall, D. O. (2005). Computerized adaptive testing. Encyclopedia of social measurement, 1, 429-438.
Baker, F. B. (2001). The basics of item response theory: ERIC.
DeMars, C. (2010). Item response theory: Oxford University Press.
Reeve, B. B.. & Fayers, P. (2005). Applying item response theory modeling for evaluating questionnaire item and scale properties. Assessing quality of life in clinical trials: Methods of practice, 2, 55-73.
Stemler, S. E., & Naples, A. (2021). Rasch measurement v. item response theory: Knowing when to cross the line. Practical Assessment, Research & Evaluation, 26, 11.
van der Linden, W. J., & Hambleton, R. K. (2013). Handbook of modern item response theory: Springer Science & Business Media.
Embretson, S. E., & Reise, S. P. (2013). Item response theory: Psychology Press.
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory (Vol. 2): Sage.
Baker, F. B., & Kim, S.-H. (2004). Item response theory: Parameter estimation techniques: CRC press.
Doignon, J.-P., & Falmagne, J.-C. (2016). Knowledge spaces and learning spaces.
Behjati, S., & Beigy, H. (2020). Improved K2 algorithm for Bayesian network structure learning. Engineering Applications of Artificial Intelligence, 91, 103617.
Doble, C., Matayoshi, J., Cosyn, E., Uzun, H., & Karami, A. (2019). A data-based simulation study of reliability for an adaptive assessment based on knowledge space theory. International Journal of Artificial Intelligence in Education, 29(2), 258-282.
Muñoz-Merino, P. J., Novillo, R. G., & Kloos, C. D. (2018). Assessment of skills and adaptive learning for parametric exercises combining knowledge spaces and item response theory. Applied Soft Computing, 68, 110-124.
