The Use of Probabilistic Models in the Assessment of Mastery

Abstract

Descriptions are presented of two related probabilistic models that can be used for making classification decisions with respect to mastery of specific concepts or skills. Included are the development of procedures for: (a) assessing the adequacy of “fit” provided by the models; (b) identifying optimal decision rules for mastery classification; and (c) identifying minimally sufficient numbers of items necessary to obtain acceptable levels of misclassification.

Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data

“…Applications of the DINA model along with MCMC algorithms for estimation are given in Junker and Sijtsma (2001), Tatsuoka (2002), and de la Torre and Douglas (2004). The DINA model is also discussed in Macready and Dayton (1977), Haertel (1989), and Doignon and Falmagne (1999).…”

Cluster Analysis for Cognitive Diagnosis: Theory and Applications

“…The DINA model extends the work of Macready and Dayton (1977), which considers a two-class version of it for assessing mastery of a skill. The item response function of the DINA model is…”

Recovering a Probabilistic Knowledge Structure by Constraining its Parameter Space

“…The more closely related one is the so-called DINA (Deterministic Inputs Noisy ANDgate) model (Haberman, 1979;Junker, 2001;Macready & Dayton, 1977); see also in this connection, Maris (1999). There are essentially two main differences between the DINA and the BLIM.…”