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…

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…

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…

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…

Computational Thinking (CT) is viewed as a set of foundation skills required to solve problems efficiently and effectively, with or without the use of technology. It has also been argued that CT can provide connections between computing and other core curriculum areas which can be beneficial for student learning outcomes. However, there are still…

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 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…

We present an interactive spreadsheet that supports teaching essential concepts in classification using the logistic regression (LoR) model for binary classification. The interactive spreadsheet demonstrates the capabilities of LoR by integrating computation with visualization. Students will reinforce concepts like probabilities, maximum…

This article introduces an innovative sampling scheme, the median sampling (MS), utilizing individual observations over time to efficiently estimate the mean of a process characterized by a symmetric (non-uniform) probability distribution. The mean estimator based on MS is not only unbiased but also boasts enhanced precision compared to its simple…

Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units…

As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…