Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi wa Kipengee cha Kujiendesha kwa Kompyuta× | Nadharia ya Itikio la Kipengee (IRT)× | |
|---|---|---|
| Nyanja | Saikometriki | Saikometriki |
| Familia | Latent structure | Latent structure |
| Mwaka wa asili≠ | 1970s–1990s | 1952–1968 |
| Mwanzilishi≠ | Lord, Weiss, and colleagues in psychometric research on adaptive testing | Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models) |
| Aina≠ | Item calibration and evaluation | Probabilistic measurement model |
| Chanzo asilia≠ | van der Linden, W. J. & Glas, C. A. W. (Eds.) (2000). Computerized Adaptive Testing: Theory and Practice. Kluwer Academic Publishers. ISBN: 978-0792365556 | Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley. link ↗ |
| Majina mbadala | CAT item analysis, adaptive item calibration, IRT-based CAT item evaluation, adaptive item parameter estimation | IRT, latent trait theory, item characteristic curve theory, modern test theory |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Computerized adaptive test item analysis evaluates and calibrates items intended for use in adaptive testing environments. Unlike fixed-form analysis, it accounts for the non-random item exposure inherent in adaptive administration, using item response theory to estimate item parameters, information functions, and exposure rates across the ability continuum. | Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons. |
| ScholarGateSeti ya data ↗ |
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