Given sample data, how do you calculate the value of a parameter? While this question is impossible to answer, it is frequently encountered in statistics classes when students are introduced to the distinction between a sample and a population (or between a statistic and a parameter). It is not uncommon for teachers of statistics to also confuse…

Recently, computational thinking (CT) has gained importance in education systems worldwide, specifically the CT training of pre-service teachers. This study conducted a systematic literature analysis (2011-2021) of 38 works on pre-service teachers' CT based on Web of Science, Science Direct, and Google Scholar databases. The results were as…

This study investigated the effect of video lecture types on the performance of students in computational problem-solving practices. A total of 19 university students participated in the computational problem-solving practices that mostly required declarative knowledge, and 22 university students participated in the computational problem-solving…

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and…

Back random responding (BRR) behavior is one of the commonly observed careless response behaviors. Accurately detecting BRR behavior can improve test validities. Yu and Cheng (2019) showed that the change point analysis (CPA) procedure based on weighted residual (CPA-WR) performed well in detecting BRR. Compared with the CPA procedure, the…

To address the computational constraints of parameter estimation in the polytomous Cognitive Diagnosis Model (pCDM) in large-scale high data volume situations, this study proposes two two-stage polytomous attribute estimation methods: P_max and P_linear. The effects of the two-stage methods were studied via a Monte Carlo simulation study, and the…

Computational Thinking (CT) has become popular in recent years and has been recognized as an essential skill for everyone in the digital age. CT literature, however, is at an early stage of development, and there is no consensus among researchers/scholars in the field. To date, many have been unable to concretely explain what CT is, or how to…

Computational thinking (CT) has received growing interest as a research subject in the last decade, with research contributions attempting to capitalize on the benefits that CT may provide. This study included a systematic analysis aimed at revealing current trends in the CT subject, identifying educational interventions, and emerging assessment…

Despite the increasing number of coding initiatives to promote computational thinking (CT), their main focus on in-service teachers in large school districts of the big cities far from exemplifies opportunities for preservice teachers (PSTs) to learn how to promote it in rural elementary school settings. As a preliminary step, this research…

Despite the widespread adoption of computational thinking (CT) across educational levels, challenges persist in its assessment due to diverse definitions, frameworks, and practical applications in classroom settings. This meta-synthesis investigates the assessment of computational thinking (CT) in primary and secondary education, synthesising…

Computational thinking (CT) has the potential to enhance learning when integrated into mathematical classroom activities. Teachers are being asked to include CT concepts in their core disciplines; however, there is an open question as to how best to equip teachers to integrate CT into their practice. Oftentimes teacher candidates enter math and…

Although computational thinking has most often been associated with the science, technology, engineering, and math education fields, our research takes a first step toward documenting student outcomes associated with integrating and assessing computational thinking in the social studies. In this study, we pursued an embedded research design,…

Bayesian estimations of complex regression models with high-dimensional parameter spaces require advanced priors, capable of addressing both sparsity and multicollinearity in the data. The Dirichlet-horseshoe, a new prior distribution that combines and expands on the concepts of the regularized horseshoe and the Dirichlet-Laplace priors, is a…

This dissertation used synthetic datasets, semi-synthetic datasets, and a real-world dataset from an educational intervention to compare the performance of 15 machine learning and multiple imputation methods to estimate the individual treatment effect (ITE). In addition, it examined the performance of five evaluation metrics that can be used to…

This paper presents a systematic literature review of professional development programs in computational thinking (CT). CT has emerged as an essential set of skills that everyone should develop to participate in a global society. However, there were no pre-service or in-service teacher programs to integrate CT into the K-12 classrooms until very…