Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model…

Purpose: The purpose of this study is to determine the competency characteristics required for the employment of master's degree students in educational technology. Design/methodology/approach: A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were…

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Scientific creativity plays an essential role in science education as an advanced cognitive ability that inspires students to solve scientific problems inventively. The cultivation of scientific creativity relies heavily on effective assessment. Typically, human raters manually score scientific creativity using the Consensual Assessment Technique…

Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This…

This research investigates the linkage between training evaluation, learning design and training transfer. A new training evaluation model, (i.e., learning-transfer evaluation model [LTEM]), was used to examine its ability to provide evaluative evidence through robust assessments in pre-, post- and delayed assessments. The model was used to…

This study analyzed studies done in Turkey in the context of curriculum evaluation (CE) by asking, "How is it made? The study was carried out in two stages. In the first stage, the document analysis method used 215 theses written between 1991 and 2020 on CE were analyzed according to the "thesis review form." In the second stage,…

In recent years, proponents of configurational comparative methods (CCMs) have advanced various dimensions of robustness as instrumental to model selection. But these robustness considerations have not led to computable robustness measures, and they have typically been applied to the analysis of real-life data with unknown underlying causal…

Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like SRMR, RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the…

We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and…

The roles in traditional mentoring dyads are well known across both academic and professional contexts (Dawson, 2014). Despite the universality of these relationships, the way mentorship is evaluated in these relationships is fractured. Evaluation is limited to singular voices, singular points in time and simplified metrics to capture the journey…

Validation is needed for any newly developed model or framework because it requires several real-life applications. The investment made into e-learning in medical education is daunting, as is the expectation for a positive return on investment. The medical education domain requires data-wise implementation of e-learning as the debate continues…