A Semi-supervised Learning-Based Diagnostic Classification Method Using Artificial Neural Networks.

The purpose of cognitive diagnostic modeling (CDM) is to classify students' latent attribute profiles using their responses to the diagnostic assessment. In recent years, each diagnostic classification model (DCM) makes different assumptions about the relationship between a student's response pattern and attribute profile. The previous research studies showed that the inappropriate DCMs and inaccurate Q-matrix impact diagnostic classification accuracy.

Assessing Growth in a Diagnostic Classification Model Framework.

A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time.

Development and Psychometric Evaluation of the Autism Stigma and Knowledge Questionnaire (ASK-Q).

ASD knowledge deficits contribute to disparities in the timing and quality of ASD services. To address the limitations with existing measures of ASD knowledge, we developed and examined the Autism Stigma and Knowledge Questionnaire (ASK-Q), which comprehensively assesses multiple subdomains of ASD knowledge while maintaining strong psychometric support and cross-cultural utility. ASK-Q items derived from the published research are organized into four subscales: (i) diagnosis, (ii) etiology, (iii) treatment, and (iv) stigma.

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model.

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or are measured by each item are a core element of the diagnostic assessment design.