Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DINA modelis× | Kognitīvā diagnostiskā datorizētā adaptīvā testēšana× | |
|---|---|---|
| Nozare | Psihometrija | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2001 | 2007 |
| Autors≠ | Brian Junker, Klaas Sijtsma | Xueli Xu, Jean-Paul Fox |
| Tips≠ | Discrete latent class model | Skill-adaptive testing with psychometric diagnostic classification |
| Pirmavots≠ | Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272. DOI ↗ | Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about examinees' strengths and weaknesses: Cognitive diagnostic information in TIMSS. Journal of Educational Evaluation for Policy Analysis, 24(1), 79-100. link ↗ |
| Citi nosaukumi | DINA | CD-CAT |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The DINA Model (Deterministic Inputs, Noisy Outputs) is a cognitive diagnostic model developed by Junker and Sijtsma (2001) that classifies examinees into latent skill classes based on their item response patterns. DINA assumes a deterministic relationship between skill mastery and correct responses, with probabilistic error accounting for guessing and slips. | Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines computerized adaptive testing (CAT) with cognitive diagnostic models (CDMs) to efficiently assess students' specific skill profiles. Rather than producing a single overall ability score, CD-CAT adaptively selects items to quickly identify which skills a student has mastered and which need development. |
| ScholarGateDatu kopa ↗ |
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