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Showing 1 to 15 of 26 results Save | Export
Michael L. Chrzan; Francis A. Pearman; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
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…
Descriptors: Artificial Intelligence, Electronic Learning, School Districts, School Closing
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Junxian Shen; Hongfeng Zhang; Jiansong Zheng – Psychology in the Schools, 2024
Online learning is becoming more and more common, so how to maintain learners' online learning engagement is very important. This study aims to explore the impact of future self-continuity on college students' online learning engagement and its underlying mechanism of action. We utilized the Future Self-Continuity Questionnaire, the Learning…
Descriptors: College Students, Learner Engagement, Electronic Learning, Predictor Variables
Janet L. Randerson – ProQuest LLC, 2023
The purpose of this quantitative correlational-predictive study was to study if and to what extent emotional intelligence and race, individually and/or combined, predict self-efficacy among teachers teaching online at US-based colleges and universities. The theoretical foundation for this study was based on Salovey and Mayer's 1990 Theory of…
Descriptors: College Faculty, Online Courses, Electronic Learning, Emotional Intelligence
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Abdessamad Chanaa; Nour-eddine El Faddouli – Journal of Education and Learning (EduLearn), 2024
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…
Descriptors: Electronic Learning, Evaluation Methods, Artificial Intelligence, Taxonomy
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Zhengze Li; Hui Chen; Xin Gao – Education and Information Technologies, 2024
Online supplementary education has been prevalent in recent years due to the advent of technology (e.g., live streaming) and the COVID-19 pandemic. However, the performance of students in this mode of education varies greatly, and the underlying reasons are yet to be investigated. This study aims to understand the impact of various factors and…
Descriptors: Predictor Variables, Elementary School Students, Electronic Learning, Supplementary Education
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Kuadey, Noble Arden; Mahama, Francois; Ankora, Carlos; Bensah, Lily; Maale, Gerald Tietaa; Agbesi, Victor Kwaku; Kuadey, Anthony Mawuena; Adjei, Laurene – Interactive Technology and Smart Education, 2023
Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology…
Descriptors: Foreign Countries, College Students, Learning Management Systems, Student Behavior
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Ouyang, Fan; Zheng, Luyi; Jiao, Pengcheng – Education and Information Technologies, 2022
As online learning has been widely adopted in higher education in recent years, artificial intelligence (AI) has brought new ways for improving instruction and learning in online higher education. However, there is a lack of literature reviews that focuses on the functions, effects, and implications of applying AI in the online higher education…
Descriptors: Artificial Intelligence, Electronic Learning, Higher Education, Literature Reviews
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
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…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
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Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
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.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Cody Gene Singer – ProQuest LLC, 2023
College and university enrollment has decreased nationwide every year for more than a decade as educational consumers increasingly question the value of higher education and discover alternatives to the traditional university system. Enrollment professionals seeking growth are tasked to develop and implement innovative solutions to address…
Descriptors: Data Collection, Predictor Variables, Electronic Learning, Enrollment
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Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
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Schmucker, Robin; Wang, Jingbo; Hu, Shijia; Mitchell, Tom M. – Journal of Educational Data Mining, 2022
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…
Descriptors: Academic Achievement, Electronic Learning, Artificial Intelligence, Predictor Variables
Satyadhar Joshi – Online Submission, 2025
The rapid emergence of agentic artificial intelligence (AI) systems represents a paradigm shift in military operations, demanding fundamental transformation of US military education. This paper presents a comprehensive framework for reskilling and redesigning military education to address critical workforce readiness gaps in the era of autonomous…
Descriptors: Job Skills, Skill Development, Job Training, Military Personnel
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Yu Cui; Lingjie Tang; Fang Fang – Journal of Computer Assisted Learning, 2025
Background Study: With the rapid transition to remote learning necessitated by the closure of traditional educational infrastructures globally, the arena of informal digital learning of English (IDLE) has received much attention, particularly among English as a Foreign Language (EFL) learners in China. Objective: This study explores how…
Descriptors: Electronic Learning, Artificial Intelligence, Predictor Variables, Informal Education
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Hilpert, Jonathan C.; Greene, Jeffrey A.; Bernacki, Matthew – British Journal of Educational Technology, 2023
Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform…
Descriptors: Learning Theories, Independent Study, Artificial Intelligence, Biology
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