In this study, associations among emotional intelligence, background variables, and academic performance in prelicensure nursing students enrolled in associate and baccalaureate degree nursing programs are explored. A gap in the literature exists regarding the specific background variables utilized in this study and the population. This study used…

Recently, artificial intelligence (AI) technologies have been widely used in the field of education, and artificial intelligence in education (AIEd) has gained increasing attention. However, no quantitative meta-analysis has been conducted on the overall effectiveness of AI on learning achievement and learning perception. To close this research…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

South African undergraduate university students face many unique challenges that put them at risk of developing mental health concerns such as depression, anxiety, stress, suicidal ideation, and posttraumatic stress disorder. However, psychological well-being has been found to play an essential role when it comes to effectively coping with and…

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…

Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable…

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant…

Based on the Job Demands-Resources theory, this research investigated the multiple mediating role of special education teachers' social support and work engagement in the relationship between their emotional intelligence and job performance. Data of 710 Chinese mainland teachers in special education schools were analyzed. The results showed that…

There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…

It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…

Aim/Purpose: This study investigates the key factors influencing preschool teachers' sustained use of Artificial Intelligence-Generated Content (AIGC) technology in educational settings. While prior research has extensively examined initial adoption, little attention has been given to understanding the continuous intention of preschool teachers…

Preschool pre-service teachers need to be prepared for the age of artificial intelligence (AI) as part of the teaching force; however, much research is still needed in this area. This study aimed to explore the structural relationships in pre-service preschool teachers' perceived AI readiness, focusing on AI literacy, AI anxiety, AI confidence and…

This study provides the first analyses connecting individual differences in infant attention to reading achievement through the development of executive functioning (EF) in infancy and early childhood. Five-month-old infants observed a video, and peak look duration and shift rate were video coded and assessed. At 10 months, as well as 3, 4, and…

This present work uses artificial neural networks (ANNs) to examine the association between various dimensions of coaching leadership and turnover Intention. The coaching leadership data were collected from 194 employees across multiple schools in Korea. The ANN models are capable of higher predictive accuracy than conventional linear regression…

Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based…