The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning…

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…

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…

Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…

This study explores the effectiveness of machine learning and eye movement features in predicting Chinese reading proficiency. Unlike previous research, which focused on one or two specific levels of eye movement features, this study integrates passage-, sentence- and word-level eye movement features to predict reading proficiency. By analyzing…

A neurological disorder, along with several behavioral issues, may be to blame for a child's subpar performance in the academic journey (such as anxiety, depression, learning disorders, and irritability). These symptoms can be used to diagnose children with ASD, and supervised machine learning models can help differentiate between ASD traits and…

Purpose: Researchers have developed a constellation model of decodingrelated reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation…

Ecological intelligence is a comprehensive understanding that aims to create an awareness regarding how human activities affect ecosystems and to promote preventing unconscious consumption behaviors that would lead to a sustainable life. It enables us to take social, economic and environmental responsibility, also to act cooperatively and…

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…

Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…

This study examined the relationships between word reading and rapid automatized naming (RAN) for objects and letters in Arabic-speaking children with and without dyslexia to determine potential modulating effects of color on naming by comparing children's performance on color and black-white RAN plates. Participants were 114 Arabic-speaking third…

The advancement of technologies has resulted in the boost of a popular chatbot software -- ChatGPT. It is ripe with potential, yet has introduced various challenges, especially in the world of education. This paper aims to explore how TEFL (Teaching English as a foreign language) students perceive the usefulness and ease of using ChatGPT in regard…

Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their…

Background/Context: Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like…