A decision consistency (DC) index is an estimate of the consistency of a classification decision on an exam. More specifically, DC estimates the percentage of examinees that would have the same classification decision on an exam if they were to retake the same or a parallel form of the exam again without memory of taking the exam the first time.…

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping,…

In latent growth curve modeling (LGCM), overall fit indices have garnered increased disputation for model selection, and model fit evaluation based on the mean structure has becoming popularity. The present study developed a versatile fit index, named Weighted Root Mean Squared Errors (WRMSE), based on individual case residuals (ICRs) with the aim…

In many large-scale testing programs, examinees are frequently categorized into different performance levels. These classifications are then used to make high-stakes decisions about examinees in contexts such as in licensure, certification, and educational assessments. Numerous approaches to estimating the consistency and accuracy of this…

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled…

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis.…

There is a need to continue to amplify the value of occupational therapy for individuals with disabilities who are transitioning from secondary education to adult settings, such as vocational and post-secondary educational environments. Literature evidences a lack of practitioner knowledge of assessments and evaluations within the scope of…

Latent class analysis (LCA) is a widely used technique for detecting unobserved population heterogeneity in cross-sectional data. Despite its popularity, the performance of LCA is not well understood. In this study, we evaluate the performance of LCA with binary data by examining classification accuracy, parameter estimation accuracy, and coverage…

Linguistic categories can impact visual perception. For instance, learning that two objects have different names can enhance their discriminability. Previous studies have identified a typical pattern of categorical perception, characterized by faster discrimination of stimuli from different categories, a neural mismatch response during early…

Despite numerous studies about language learning strategies (LLSs), many learners still misunderstand their effectiveness, thinking they require too much effort for minimal gain. Additionally, students have varied and conflicting preferences for LLSs, and factors like cultural background influence their choices, indicating a need for more research…

In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the…

To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to…

This case examines the tax implications of various not-for-profit statuses available to an organization. Students are presented with a case that considers whether the organization currently classified as a 501(c)(7) organization is properly classified, or should be classified as a 501(c)(3) organization, which would allow its members to take…

The p-factor is a construct that is thought to explain and maybe even cause variation in all forms of psychopathology. Since its 'discovery' in 2012, hundreds of studies have been dedicated to the extraction and validation of statistical instantiations of the p-factor, called general factors of psychopathology. In this Perspective, we outline five…