A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…

In the contemporary digitalised knowledge society, work-integrated professional development is an important and continuous activity. Continuous professional development should preferably be a hybrid format, where academia collaborates with industry and the surrounding society in a multi-directed exchange of ideas. Continuous professional…

Research has shown that multiple-indicator multiple-cause (MIMIC) models can result in inflated Type I error rates in detecting differential item functioning (DIF) when the assumption of equal latent variance is violated. This study explains how the violation of the equal variance assumption adversely impacts the detection of nonuniform DIF and…

Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of…

To develop a love of reading in the young, teachers need rich repertoires of children's literature and other texts. However, the significance of this subject knowledge is rarely given the attention it deserves in policy, practice, or training contexts. This article, drawing on survey data from England and Finland, underlines these concerns. It…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of…

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR)…

Mathematical modelling has acquired relevance in different fields at an international level, both in education and research. This article states that, throughout the construction of the theoretical corpus of this mathematical process and competency -- among others -- two big issues have occurred: one of terminological nature since the definitions…

The objectives of research were to develop the new normal administration model according to the principles of good governance for schools under the Office of Primary Educational Service Area in Sakon Nakhon Province. The findings were as follows: I) Components of new normal administration for schools, consisting of 8 components: 1) New Normal…

The tumult of the early 21st century created a need for leaders who act responsibly in times of crises. Responsible leadership differs from other leadership styles in its attention to the engagement of diverse stakeholders and decisions that support the common good. In the growing field of responsible leadership studies, scholars call for more…

We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the…

Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate…

Numerous methods exist to determine the optimal number of classes when using latent profile analysis (LPA), but none are consistently correct. Recently, the likelihood incremental percentage per parameter (LI3P) was proposed as a model effect-size measure. To evaluate the LI3P more thoroughly, we simulated 50,000 datasets, manipulating factors…

The limitation of conventional visual acuity assessment, which primarily focuses on individual eye performance (monocular visual acuity tests). This study addresses this limitation by emphasizing the importance of binocular vision, where both eyes work together. Binocular vision provides numerous advantages, such as improved depth perception, a…