Explanatory Cognitive Diagnostic Models: Incorporating Latent and Observed Predictors

Abstract

Large-scale educational testing data often contain vast amounts of variables associated with information pertaining to test takers, schools, or access to educational resources-information that can help relationships between test taker performance and their learning environment. This study examines approaches to incorporate latent and observed explanatory variables as predictors for cognitive diagnostic models (CDMs). Methods to specify and simultaneously estimate observed and latent variables (estimated using item response theory) as predictors affecting attribute mastery were examined. Real-world data analyses were conducted to demonstrate the application using large-scale international testing data. Simulation studies were conducted to examine the recovery and classification for simultaneously estimating multiple latent (using dichotomous and polytomous items as indicators for the latent construct) and observed predictors for varying sample sizes and number of attributes. Results showed stable parameter recovery and consistency in attribute classification. Implications for latent predictors and attribute specifications are discussed.

The Development of an Instrument for Longitudinal Learning Diagnosis of Rational Number Operations Based on Parallel Tests

“…Currently, there are many applications use cross-sectional LDMs to diagnose individuals’ learning status in the field of mathematics because the structure of mathematical attributes is relative clear to be identified, such as fraction calculations ( Tatsuoka, 1983 ; Wu, 2019 ), linear algebraic equations ( Birenbaum et al, 1993 ), and spatial rotations ( Chen et al, 2018 ; Wang et al, 2018 ). Some studies also apply cross-sectional LDMs to analyze data from large-scale mathematical assessments (e.g., George and Robitzsch, 2018 ; Park et al, 2018 ; Zhan et al, 2018 ; Wu et al, 2020 ). However, most of these application studies use cross-sectional design and cannot track the individual growth of mathematical ability.…”

Measuring Skill Growth and Evaluating Change: Unconditional and Conditional Approaches to Latent Growth Cognitive Diagnostic Models

“…Dayton and Macready (1988) introduced the use of covariate to affect the attribute. Park et al (2017) included both observed and latent explanatory variables as covariates in the explanatory CDM to inform learning and practice. Thus, this approach is meaningful in the CDM for its diagnostic purpose.…”

Reporting Subscore Profiles Using Diagnostic Classification Models in Health Professions Education

“…Moreover, classification indices based on posterior probabilities were used to examine the quality of subscore classification (Clogg, 1995). In addition to the number of items linked to a subscore domain in the Q-matrix, the response pattern of examinees impact the quality of classification (Park & Lee, 2014;Park et al, 2018;Templin & Bradshaw, 2013).…”

“…Recent trends in the assessment literature have called for assessment frameworks that inform learning-directing a shift toward assessment for learning in addition to previous uses of assessments of learning (Eva et al, 2016). However, most assessments, particularly high-stakes assessments, are designed and analyzed to provide only a single overall test score, limiting the scope of information conveyed to learners and educators (Lee, de la Torre, & Park, 2011;Park, Xing, & Lee, 2018). For example, a single pass-fail decision or overall percent correct scores may not provide sufficient information to facilitate improvement or change.…”