By applying educational data mining methods to big data related to large-scale exams, functional relationships are discovered in a basic sense and hidden pattern(s) can be revealed. Within the scope of the research, to show how the self-organizing map (SOM) method can be used in terms of educational data mining, how SOM differs from other…

Poor program implementation constitutes one explanation for null results in trials of educational interventions. For this reason, researchers often collect data about implementation fidelity when conducting such trials. In this article, we document whether and how researchers report and measure program fidelity in recent cluster-randomized trials.…

Concept maps are powerful tools used to reveal challenges in students' learning. However, their use introduces complexities when a large group of students' conceptualizations need to be examined. In this study, concept maps of 344 general chemistry students were analyzed after grouping them based on achievement in chemistry, math proficiency, and…

In seeking to close equity gaps within a first-year student seminar course, course designers leveraged emerging research on intrapersonal competency cultivation, known to significantly predict student success across diverse students (NAS, 2018). After re-designing the course to intentionally cultivate specific intrapersonal competencies,…

Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

Academic scholars face a difficult challenge when attempting to grasp the intricate world of mathematics. The complexity of mathematical concepts often lies hidden beneath layers of formulas and procedures, obscuring their true essence. Traditional educational resources often fall short in conveying the profound meaning behind these concepts,…

In this study, it is aimed to examine the relationship between prospective teachers' occupational anxiety and technological pedagogical content knowledge. Correlational model was used in this study. The participants consisted of 481 prospective teachers studying at Sakarya University Faculty of Education in the spring term of 2018-2019 academic…

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the…

Tests administered in studies of student achievement often have a certain amount of not-reached items (NRIs). The propensity for NRIs may depend on the proficiency measured by the test and on additional covariates. This article proposes a semiparametric model to study such relationships. Our model extends Glas and Pimentel's item response theory…

With the U.S. adult education system providing education services to millions of immigrants annually, understanding the unique skills and assets among adult immigrant learners is important. Using data from the U.S. Program for the International Assessment of Adult Competencies, this study used data on immigrants (n = 1,873) to identify latent…

The unskilled and unaware effect occurs when low-performing individuals overestimate their performance, while high performers underestimate it. The aim of the present study was to examine an occurrence of the unskilled and unaware effect in a self-assessment of creative performance, linking this phenomenon to creative metacognition (CMC) research.…

This study investigated developmental trajectories of reading and math using latent-growth-curve analyses across multiple academic skills, measures, and multiple time periods within a single sample. Reading-related growth was marked by significant individual differences during the early elementary-school period and nonsignificant individual…

A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers…

We study flexible Bayesian methods that are amenable to a wide range of learning problems involving complex high dimensional data structures, with minimal tuning. We consider parametric and semiparametric Bayesian models, that are applicable to both static and dynamic data, arising from a multitude of areas such as economics, finance and…

Background: Network meta-analysis (NMA) of survival data with a multidimensional treatment effect has been introduced as an alternative to NMA based on the proportional hazards assumption. However, these flexible models have some limitations, such as the use of an approximate likelihood based on discrete hazards, rather than a likelihood for…