Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…

This study examined the efficiency of using the same rating scale categories in measuring affective constructs for students with distinctive levels of achievement. Data used in this study came from the Trends in Mathematics and Science Study (TIMSS) 2011, as a case, on the three scales that were designed to measure eighth graders' attitudes…

This paper introduces an approach that uses latent class analysis to identify cut scores (LCA-CS) and categorize respondents based on context scales derived from largescale assessments like PIRLS, TIMSS, and NAEP. Context scales use Likert scale items to measure latent constructs of interest and classify respondents into meaningful ordered…

This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…

Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB)…

Educational Data Mining (EDM) is an important tool in the field of classification of educational data that helps researchers and education planners analyse and model available educational data for specific needs such as developing educational strategies. Trends International Mathematics and Science Study (TIMSS) which is a notable study in…

The quality of diagnostic profiles and probability assignment depends on the validity of the proposed attributes and Q-matrix. The rule-space method (RSM), one of diagnostic classification models, provides the quality indices of diagnostic profiles, such as the classification rate and the squared Mahalanobis distance. The study aims to further…

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…

Large-scale assessments are generally designed for summative purposes to compare achievement among participating countries. However, these nondiagnostic assessments have also been adapted in the context of cognitive diagnostic assessment for diagnostic purposes. Following the large amount of investments in these assessments, it would be…

Families are active agents in school systems and apply different strategies of educational advantage to help their children succeed at school. These strategies are planned and enacted by families with their children in mind, but they are always a response to the broader education system design. This article explores how through their strategies…

In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or "Q-matrix") designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international…

Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…

A primary purpose of cognitive diagnosis models (CDMs) is to classify examinees based on their attribute patterns. The Q-matrix (Tatsuoka, 1985), a common component of all CDMs, specifies the relationship between the set of required dichotomous attributes and the test items. Since a Q-matrix is often developed by content-knowledge experts and can…

The aim of this research was to assess the classification of science test items of TIMSS grade 8 based on higher order thinking skills (HOTS) and determine whether those classified-science test items can be an assessment tool in science class. Sixteen sample test items of HOTS were chosen from 37 reasoning items of TIMSS 1999, 2003, and 2011;…

This study provides the results of a latent class analysis (LCA) using data from the Trends in International Mathematics and Science Study -- 2011 (TIMSS-2011) with a focus on the 8th grade mathematics section. The study presents the analysis of item data with Mplus 7.31 to determine if results obtained yielded distinct latent subgroups. The data…