Research on educational television has overwhelmingly investigated the impact of viewing on children's knowledge acquisition. However, this content might influence other important outcomes, such as children's interest in learning about new topics. To investigate this possibility, we invited parents of 3- to 8-year-old children (N = 83) to answer…

Predictive models of student success can provide timely information to inform interventions in K-12 and higher education. However, the design and implementation of these predictive models require various stakeholders to make decisions about the prediction target, data sources, processing, training, models, and deployment strategies. These choices…

In the post-pandemic era, universities in multi-stakeholder environments have increased research interest in responsible leadership and organizational citizenship behavior for the environment (OCBE) on university campuses, as a means to address global environmental degradation and enhance the environmental performance of higher education…

Many large-scale assessments model proficiency with a latent regression on contextual variables. Item-response data are used to estimate the parameters of the latent variable model and are used in conjunction with the contextual data to generate plausible values of individuals' proficiency attributes. These models typically incorporate numerous…

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an…

In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…

The purpose of this study is to determine if personality traits can predict the coaching intervention style preferences of teachers in the United States of America. The rationale for this study is to gain a better understanding of whether there are personality traits that are better suited to particular coaching techniques/methods. To address this…

Purpose: Academic anxiety is a common phenomenon in the college student population, which has an important impact on students' psychological health and academic performance. Therefore, by exploring the effects of college students' professional commitment and achievement goal orientation variables on academic anxiety, it helps to understand…

This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

School districts increasingly gauge school quality with surveys that ask about school climate and student engagement. We use data from New York City's middle and high schools to compare the long-run predictive validity of surveys with that of conventional test score value-added models (VAMs). Our analysis leverages the New York school match, which…

There is a common assumption that changes in developmental movement patterns (process) leads to better skill outcome performance (product); however, limited longitudinal data evaluate this assumption. This study examined (a) the longitudinal relationship among process and product motor skill scores across early childhood (3.5-6 years) and (b) the…

Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic…

When tutoring students it is useful to be able to predict whether they are succeeding as early as possible. This paper compares multiple methods for predicting from sequential interaction data whether a student is on a successful path. Predicting students' future performance and intervening has shown promise in improving learner outcomes and…

Purpose: This research employs machine learning and mediation analysis, along with path analysis, to investigate the correlations between factors such as body mass index (BMI) and the occurrence of diabetes and heart disease among the Indian population. The objective is to enhance models that are specifically designed to accommodate lifestyles,…

In the process of school self-evaluation (SSE), teachers are often asked to provide information on the school's functioning by completing questionnaires. However, respondents can leave one item or a series of items unanswered, after which they continue completing the questionnaire. This phenomenon of item nonresponse is not necessarily problematic…