Implementing the Blended Learning model was identified as having a major influence on achieving learning objectives in the classroom. Blended learning facilitates different times and types of student learning so students can learn according to their needs. The purpose of this study is to define the blended learning effectiveness used at MTs…

This paper applies the well-known cognitive bias of loss aversion from behavioural economics to student decisions over engagement with mathematically demanding coursework. This bias is shown to predict behaviour that is consistent with mathematics anxiety in a dynamic model of student engagement. It is shown that these forces can imply…

Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not easy for students to obtain question banks. To solve…

This study examined the visual design processes and strategies used by design experts and non-design college students when composing an infographic. All participants created an infographic on a personal computer and were asked to think aloud as they performed the task. The goal was to identify differences in the strategies utilised to produce…

The development of metaknowledge about models (MKM) and metaknowledge about the modelling process (MKP) are important in pre-service science teachers (PSTs) education. MKM refers to knowledge about the model's entities and its purposes, while MKP refers to knowledge about the components and structure of the modelling process. Assessing MKM and MKP…

This paper presents a model of recurrent multinomial sequences. Though there exists a quite considerable literature on modeling autocorrelation in numerical data and sequences of categorical outcomes, there is currently no systematic method of modeling patterns of recurrence in categorical sequences. This paper develops a means of discovering…

In recent years, educators, administrators, policymakers, and measurement experts have called for assessments that support educators in making better instructional decisions. One promising approach to measurement to support instructional decision-making is diagnostic classification models (DCMs). DCMs are flexible psychometric models that…

Although the scholarship of teaching and learning (SoTL) has been described as the signature pedagogy of educational development (a professional field focused on support for teaching and learning in higher education), there is little systematic evidence of the scale and scope of the SoTL support practices that centers for teaching and learning…

Understanding complex physiological processes is a cornerstone of medical education, and one such fundamental concept is the regulation of the glomerular filtration rate (GFR) by Starling forces. Therefore, developing a physiologically sound educational model to demonstrate these forces can significantly enhance the learning experience for…

A practice that dates back centuries, apprenticeship is a workforce development model that has gained recent attention in the American higher education system. The researcher conducted a review of existing literature that included apprenticeships in higher education and other models of community college workforce development. Existing literature…

Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study aims to address such needs by investigating the performance of estimation methods in the bifactor models with ordered categorical data. Results support…

Within the frequentist structural equation modeling (SEM) framework, adjudicating model quality through measures of fit has been an active area of methodological research. Complicating this conversation is research revealing that a higher quality measurement portion of a SEM can result in poorer estimates of overall model fit than lower quality…

Dynamic structural equation models (DSEM) are designed for time series analysis of latent structures. Inherent to the application of DSEM is model parameter estimation, which has to be addressed in many applications by a single time series. In this context, however, the methods currently available either lack estimation quality or are…

We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean…

We explore ways in which statistics can be used to understand disease spread and support decision-making by governments. "Past performance does not guarantee future results"--we hope. We discuss and show examples from the National Science Foundation (NSF)-funded COVID-Inspired Data Science Education through Epidemiology (CIDSEE) project.…