Learning style (LS), either visual or verbal, has become debatable in the f ield of education vis-à-vis its relation to student success. However, it remains a valuable research theme for improving learning activities in the teaching of highly visual science fields, including biology, physics, and chemistry. Previous studies have primarily focused…

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 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 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…

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…

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…

This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and…

The purpose of this study is to determine the distribution of Science achievements in Life Studies Course Curricula of 2009, 2015 and 2018 regarding the knowledge and cognitive process dimensions of the Revised Bloom Taxonomy and to comparatively examine the resulting distributions. This study adopted document analysis. While conducting the…

Science excellence is associated not only with a student's inherent aptitude but also a range of contextual factors. The objective of this paper was to identify the most important contextual characteristics of top performers in scientific literacy, by simultaneously considering factors at the PISA questionnaire-based student, family, and school…

This research aimed to explore the upper-secondary school chemistry teachers' and students' conceptual structures of atomic structure by using multidimensional scaling. Atomic structure is considered to be one of the most difficult concepts in upper-secondary school chemistry course so that the conceptual structure regarding atomic structure held…

Metacognitive ability is enormously important for improving students' learning performance. However, overconfidence bias may hinder students' metacognition abilities. Therefore, in this study, we conducted an intervention to reduce or debias overconfidence among students using the KAAR (knowledge, awareness, action, and reflection) model. Ninety…