Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Моделі когнітивної діагностики (DINA / G-DINA)× | Відстеження знань× | |
|---|---|---|
| Галузь≠ | Психометрія | Освітня аналітика |
| Родина≠ | Latent structure | Machine learning |
| Рік появи≠ | 2011 | 1994 |
| Автор методу≠ | Jimmy de la Torre | Albert Corbett & John Anderson |
| Тип≠ | Latent variable diagnostic classification model | Probabilistic student modeling |
| Основоположне джерело≠ | de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199. DOI ↗ | Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. DOI ↗ |
| Інші назви | Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli | BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme |
| Пов'язані≠ | 2 | 3 |
| Підсумок≠ | Cognitive Diagnosis Models (CDMs) are a family of latent variable models designed to classify examinees according to their mastery of a set of discrete cognitive attributes or skills. The Generalized DINA (G-DINA) framework, introduced by Jimmy de la Torre in 2011, provides a unifying structure that encompasses many specific CDMs — including the DINA, DINO, ACDM, and LLM models — as special cases, enabling fine-grained diagnostic feedback beyond a single total score. | Knowledge Tracing (KT) is a student-modeling technique that estimates, at each moment in time, the probability that a learner has mastered a target knowledge component. Introduced by Corbett and Anderson in 1994, the classical Bayesian Knowledge Tracing (BKT) model treats skill acquisition as a two-state Hidden Markov Model driven by four interpretable parameters: prior knowledge, learning rate, slip, and guess. Deep variants (DKT, DKVMN, AKT) later replaced HMMs with recurrent and transformer architectures. |
| ScholarGateНабір даних ↗ |
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