Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Bayesian Knowledge Tracing× | Cognitive Diagnostic Modeling× | |
|---|---|---|
| Dziedzina | Education | Education |
| Rodzina≠ | Machine learning | Latent structure |
| Rok powstania≠ | 1994 | 2010 |
| Twórca≠ | Albert Corbett & John Anderson | Tatsuoka; DiBello, Roussos & Stout; Junker & Sijtsma; de la Torre |
| Typ≠ | Two-state hidden Markov model of latent skill mastery from response sequences | Restricted latent class models for diagnosing mastery of discrete skills |
| Źródło pierwotne≠ | 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 ↗ | Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. Guilford Press. ISBN: 9781606235270 |
| Inne nazwy≠ | BKT, Knowledge Tracing (Corbett-Anderson), Hidden Markov Knowledge Tracing, Skill Mastery Tracing | CDM, Diagnostic Classification Models, DCM, DINA / G-DINA Models |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | Bayesian knowledge tracing (BKT) is a model that estimates, after each problem a student attempts, the probability that the student has mastered the underlying skill. Introduced by Corbett and Anderson for intelligent tutoring systems, it is a two-state hidden Markov model: the latent variable is whether the skill is learned or not, and observed correct/incorrect responses update that latent state through Bayesian inference. With just four parameters — initial knowledge, learning, slip, and guess — BKT drives the mastery decisions that tell a tutor when a student can move on. | Cognitive diagnostic models (CDMs), also called diagnostic classification models, are restricted latent class models that report not a single ability score but a profile of which discrete skills or attributes a student has mastered. Each item is linked to the attributes it requires through a Q-matrix, and the model classifies every examinee into one of the possible binary mastery patterns. CDMs answer 'which specific skills does this student lack' rather than 'how much overall ability does this student have,' making them central to fine-grained diagnostic and formative assessment. |
| ScholarGateZbiór danych ↗ |
|
|