Computational thinking is a necessary skill for the 21st century. While previously examined in computer-rich settings, researchers are increasingly studying computational thinking in unplugged environments such as board games. Focusing on the African board game "Songo," this study shows that computational thinking practices are embedded…

Although research has demonstrated that introducing computational thinking (CT) benefits young children, there has been a lack of systematic professional development (PD) opportunities for preschool teachers. The current study investigated the impact of Teaching through Interaction-Plus (TTI-P) on preschool teachers' self-efficacy in teaching CT…

Contemporary society is characterized by diversity and intricacy, necessitating more meaningful learning experiences. To meet these evolving needs, the incorporation of computational systems into education must acknowledge the distinctive characteristics of learners. Therefore, we conducted a Systematic Mapping Study (SMS) to investigate…

The data we encounter in real life often contain missing values. In sampling methods, missing value imputation is done with different methods. This article proposes novel logarithmic type imputation methods for estimating the population mean in the presence of missing data under ranked set sampling (RSS). According to the determined theoretical…

Decimal numbers are generally assumed to be a straightforward extension of the base-ten system for whole numbers given their shared place value structure. However, in decimal notation, unlike whole numbers, the same magnitude can be expressed in multiple ways (e.g., 0.8, 0.80, 0.800, etc.). Here, we used a number line task with carefully selected…

Computational learning theory studies the design and analysis of learning algorithms, and it is integral to the foundation of machine learning. In the modern era, classical computational learning theory is growingly unable to catch up with new practical demands. In particular, problems arise in the following aspects: i). "scalability":…

Bounded by the statements of Feynman and Galileo, I describe certain tricks that can be useful for the teaching of physics. In particular, I describe the calculation of the center of mass (centroid) of an arc of circumference and a circular sector. For this purpose, I also use Pappus's theorems. An Appendix is available with Archimedes' method to…

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g.,…

In peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction,…

Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods…

We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete-type random variable, and a confidence interval for a large sample. Moreover, we pay…

A learner can autonomously acquire knowledge by experiencing the world, without necessarily being explicitly taught. The contents and ways of this type of real-world learning are grounded on his/her surroundings and are self-determined by computing real-world information. However, conventional studies have not modeled, observed, or understood a…

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem…

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…

Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, interpreting these functional principal components (PCs) can sometimes be challenging due to issues such as roughness and sparsity. In this dissertation,…