Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing…

Reading fluency has been a tenacious problem in our schools. Studies have addressed the national decline in reading fluency in elementary schools. Tennessee's data outlines a similar pattern of findings. Approximately one third of Tennessee students complete third grade without becoming fluent readers (Tennessee Department of Education, 2023). New…

This study aims to determine the factors influencing the efficient and successful use of LMS among university-level students. A multiperspective approach was performed using TAM3 and ISS framework to achieve the aforementioned aim. The survey was administered to 371 university students. Structural equation modeling (SEM) has been conducted to test…

Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…

Equating is a statistical procedure used to adjust for the difference in form difficulty such that scores on those forms can be used and interpreted comparably. In practice, however, equating methods are often implemented without considering the extent to which two forms differ in difficulty. The study aims to examine the effect of the magnitude…

In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…

This paper details how to effectively share three-dimensional geological models using data conversion between two mainstream mining software, Micromine and Surpac. It also discusses the impact of this conversion method on geological integrated exploration decision-making guidance. The current situation primarily manifests in the fact that both…

There is widespread concern about declining literacy skills in recent young Japanese. The present study investigated how higher-level reading and writing proficiencies are underpinned by basic literacy skills in Japanese adolescents. From a large database of the most popular literacy exams in Japan, we retrospectively analyzed word- and text-level…

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there…

What does it mean for a model to be a better model? One conceptualization, indeed a common one in Educational Data Mining, is that a better model is the one that fits the data better, that is, higher prediction accuracy. However, oftentimes, models that maximize prediction accuracy do not provide meaningful parameter estimates, making them less…

"Stochastic programs," which are programs that produce probabilistic output, are a pivotal paradigm in various areas of CS education from introductory programming to machine learning and data science. Despite their importance, the problem of automatically grading such programs remains surprisingly unexplored. In this paper, we formalize…

Feedback on student assignments plays a crucial role in steering students toward academic success. To provide feedback more promptly and efficiently, researchers are actively exploring the use of large language models (LLMs) to automatically generate feedback on student artifacts. Although the generated feedback is highly fluent, coherent, and…

Camouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report…

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate…

As diagnostic classification models become more widely used in large-scale operational assessments, we must give consideration to the methods for estimating and reporting reliability. Researchers must explore alternatives to traditional reliability methods that are consistent with the design, scoring, and reporting levels of diagnostic assessment…