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Showing 1 to 15 of 180 results Save | Export
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Luping Wang; Yun Hao; Shanshan Wang – Discover Education, 2025
In the traditional teaching mode, it is difficult for teachers to have a comprehensive understanding of each student's study, and it is also hard for them to provide targeted guidance and assistance. With the development of data collection and analysis technology, schools and educational institutions can make better use of big data technology to…
Descriptors: College Students, Predictor Variables, Scores, Academic Achievement
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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Alvin M. Ramos; Hyunkyung Lee; Romualdo A. Mabuan – International Review of Research in Open and Distributed Learning, 2025
This study investigated the relationship among e-learning readiness, learning engagement, and learning performance of preservice teachers in HyFlex learning environments. To identify the causal relationship, data collected from 776 preservice teachers at four universities in the Philippines were analyzed using structural equation modeling (SEM).…
Descriptors: Blended Learning, Preservice Teachers, Electronic Learning, Readiness
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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
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Ghai, Akanksha; Tandon, Urvashi – Education and Information Technologies, 2023
The current study investigates the interaction of Gamification, and Instructional Design to enhance the Usability of e-Learning in higher education programs. The study also examines the mediating role of Instructional design. Data were collected from a self-structured questionnaire from the academicians and was analyzed through Structural Equation…
Descriptors: Gamification, Instructional Design, Usability, Electronic Learning
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Tiana P. Johnson-Clements; Guy J. Curtis; Joseph Clare – Journal of Academic Ethics, 2025
Concerns over students engaging in various forms of academic misconduct persist, especially with the post-COVID-19 rise in online learning and assessment. Research has demonstrated a clear role of the personality trait psychopathy in cheating, yet little is known about why this relationship exists. Building on the research by Curtis et al.…
Descriptors: Pandemics, COVID-19, Cheating, Electronic Learning
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Ayça Fidan; Yasemin Koçak Usluel – Education and Information Technologies, 2024
It is pointed out that one of the main problems of online learning environments is determining whether students engage or not. As engagement is a complex and multifaceted concept, researchers have stated that engagement is effected by many factors (environmental conditions and learner characteristics) and changes according to the context. Among…
Descriptors: Online Courses, Electronic Learning, Metacognition, Emotional Response
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Miftah Arifin; Anas Ma'ruf Annizar; Moh. Khusnuridlo; Abd. Halim Soebahar; Agus Yudiawan – Journal of Education and e-Learning Research, 2025
This study examines a level and model for technology acceptability and use in online learning inside universities. The unified theory of UTAUT is used as an analysis tool. An associative quantitative method is used with a sample of 392 students. Data were collected by distributing questionnaires through a specially designed Google Form. The data…
Descriptors: Educational Technology, Electronic Learning, Technology Uses in Education, College Students
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Shixin Fang; Yi Lu; Guijun Zhang – Online Learning, 2023
Building and testing a framework of interactive and indirect predictors of student satisfaction would help us understand how to improve student online learning experience. The current study proposed that external predictors such as poor technological, environmental, and pedagogical factors would be internalized as negative psychological traits and…
Descriptors: Student Satisfaction, Electronic Learning, Educational Technology, Predictor Variables
Stephanie Dionne Connolly – ProQuest LLC, 2023
Data scientists in educational research utilize learning analytics to investigate predictors of adult learner performance or final grades in closed online courses, blended or hybrid courses, and Massive Open Online Courses (MOOCs). The purpose of this quantitative correlational study was to investigate if and to what extent a predictive…
Descriptors: MOOCs, Adult Students, Private Colleges, Learner Controlled Instruction
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Humida, Thasnim; Al Mamun, Md Habib; Keikhosrokiani, Pantea – Education and Information Technologies, 2022
Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry. The goal of this study is to scrutinize a proposed research model for predicting factors that influence student's behavioral intention to use e-learning system at Begum Rokeya University, Bangladesh.…
Descriptors: Student Behavior, Intention, Electronic Learning, College Students
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Deeva, Galina; De Smedt, Johannes; De Weerdt, Jochen – IEEE Transactions on Learning Technologies, 2022
Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by…
Descriptors: Electronic Learning, MOOCs, Dropouts, Prediction
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Gonzalo Martinez-Munoz; Miguel Angel Alvarez-Rodriguez; Estrella Pulido-Canabate – IEEE Transactions on Education, 2024
In this article, student video visualization profiles are analyzed with two objectives: 1) to identify difficult sections in videos and 2) to predict student performance based on their video visualization profiles. For identifying critical sections in videos two novel indicators are proposed. The first one is designed to measure the complexity of…
Descriptors: Video Technology, Visualization, Electronic Learning, Profiles
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Engin Demir; Huseyin Cevik – Turkish Online Journal of Distance Education, 2025
Students' attitudes towards distance education can be shaped by the compatibility of their learning styles with this new educational environment. The study aimed to investigate whether various variables and e-learning styles predict student's attitudes towards distance education. The present research was conducted on 387 students enrolled in the…
Descriptors: Student Attitudes, Electronic Learning, Educational Technology, Predictor Variables
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Anshita Chelawat; Richal Tuscano; Roshani Prasad; Seema Sant – International Journal of Learning Technology, 2025
This study aims to explore factors predicting the use of e-learning as a sustainable solution in Indian higher education institutions by employing a modified version of the technology acceptance model (TAM). An online questionnaire (n = 200), capturing post-graduate management students from the Mumbai Metropolitan Region, was analysed using…
Descriptors: Educational Technology, Electronic Learning, Graduate Students, Value Judgment
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