Data analytic methods for latent partially ordered classification models

By Curtis Tatsuoka. 2002

202 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e6796c2f76518728b32d72ac6a8781dfb26224ea

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Citations in the corpus

A Cognitive Diagnostic Modeling of Attribute Mastery in Massachusetts, Minnesota, and the U.S. National Sample Using the TIMSS 2007 (2011, methodology): “…the DINA model; a thorough discussion of the DINA model including applications and related latent classification models can be found in de la Torre and Douglas (2008), Doignon and Falmagne (1999), Haertel (1989), Junker and Sijtsma (2001), Macready and Dayton (1977), and Tatsuoka (2002).”
Sequential classification on partially ordered sets (2003, methodology): “Tatsuoka (2002)).”
Skills Diagnosis Using IRT-Based Latent Class Models (2007, methodology): “Using a data set from Tatsuoka (1990; and more recently from Tatsuoka, 2002 ), de la Torre and Douglas (2004) applied the DINA and NIDA models in the assessment of the skills used in mixed number subtraction (as part of a test given to seventh grade students).”
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art (2008, background): “…and the latent variable frameworks from which they emanate have appeared in the literature over the years (e.g., DiBello, Roussos, & Stout, 2007; NRC, 2001; Tatsuoka, 2002; Junker, 1999; Fu & Li, 2007; see also Mislevy, 2007; Nichols, Chipman, & Brennan, 1995; Rupp, 2007; Rupp & Mislevy, 2007).”
The Generalized DINA Model Framework. (2011, methodology): “The data for this illustration were a subset of the data originally described and used by Tatsuoka (1990), and more recently by Tatsuoka (2002, 2005) , and de la Torre and Douglas (2004, 2008).”
Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data (2008, methodology): “This same approach of checking if any strategies may be satisfied by an examinee’s attribute pattern and dividing the population into two equivalence classes accordingly has been applied in the POSET model in Tatsuoka (2002) .”
A Cognitive Diagnosis Model for Cognitively Based Multiple-Choice Options (2009, methodology): “Although labeled differently, other applications and discussions of the DINA model can be found in Doignon and Falmagne (1999), Haertel (1989), Macready and Dayton (1977), and Tatsuoka (2002).”
On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, background): “Tatsuoka (1990), reveals some neglected problems with respect to the classification of examinees; for example, Article”
An Empirically Based Method of Q‐Matrix Validation for the DINA Model: Development and Applications (2008, methodology): “The sequential EM-based 3-method was applied to a subset of the fraction subtraction data collected and analyzed by Tatsuoka (1990), and more recently by de la Torre and Douglas (2004) and Tatsuoka (2002).”