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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
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
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
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
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
Ottenad, Autumn H. – ProQuest LLC, 2023
This research endeavors to investigate the factors that influence satisfaction with online learning among secondary students in hybrid or blended environments in the United States. With a focus on social-emotional learning and digital citizenship, the study begins the exploration and the impact of teacher interactions, teaching presence,…
Descriptors: Predictor Variables, Secondary School Students, Student Satisfaction, Electronic Learning
Min Zhang; Qiang Jiang; Weiyan Xiong; Qi Li; Wei Zhao – Educational Technology & Society, 2024
Self-directed learning with mobile technology (SDLMT) is critical to students' learning success. However, only minimal research has been conducted on the manner by which significant aspects (e.g., selfefficacy, student engagement) are related to SDLMT. This study analyzed the answers of 485 Chinese students (seventh to ninth grades) who were…
Descriptors: Self Efficacy, Predictor Variables, Secondary School Students, Independent Study
Mehdi Haseli Songhori; Ida Fatimawati Adi Badiozaman; Reza Ahmadi – Journal of Further and Higher Education, 2024
This study used the Unified Theory of Acceptance and Use of Technology (UTAUT2) model to (i) predict factors influencing HE faculty members' acceptance of hybrid instruction and (ii) identify factors influencing the faculty members' behavioural intention to use hybrid instruction. Data were analysed using SPSS 22 and AMOS 23 software. Structural…
Descriptors: Blended Learning, Intention, College Faculty, COVID-19
Bervell, Brandford; Kumar, Jeya Amantha; Arkorful, Valentina; Agyapong, Emmanuel Manu; Osman, Sharifah – Australasian Journal of Educational Technology, 2022
Online learning environments have become a contemporary component of global tertiary education due to their affordances. These environments are hinged on internet-based learning management systems and one such tool is Google Classroom. However, empirical studies have indicated that gaps exist in determining how Google Classroom influences…
Descriptors: Virtual Classrooms, Educational Technology, Electronic Learning, Intention
Granic, Andrina – Education and Information Technologies, 2022
During the past decades a respectable number and variety of theoretical perspectives and practical approaches have been advanced for studying determinants for prediction and explanation of user's behavior towards acceptance and adoption of educational technology. Aiming to identify the most prominent factors affecting and reliably predicting…
Descriptors: Educational Technology, Technology Integration, Predictor Variables, Electronic Learning
Clemente Rodríguez-Sabiote; Ana T. Valerio-Peña; Roberto A. Batista-Almonte; Álvaro M. Úbeda-Sánchez – International Review of Research in Open and Distributed Learning, 2024
The global pandemic caused by the SARS-CoV-2 virus brought about a true revolution in the predominant teaching-learning processes (i.e., face-to-face environment) that had been implemented up to that point. In this regard, virtual teaching-learning environments (VTLEs) have gained unprecedented significance. The main objectives of our research…
Descriptors: Electronic Learning, College Students, Online Courses, Models
Ikhsan, Ridho Bramulya; Prabowo, Hartiwi; Yuniart; Simamora, Bachtiar; Ruan, Ximing; Kumar, Vikas – Journal of Educators Online, 2023
A mobile learning management system (mobile LMS) facilitates the interaction between lecturers and students to transfer knowledge flexibly. With the high possibility of universities adopting a mobile LMS into their learning systems, predicting student acceptance of mobile LMS is critical. Based on an extension of the unified theory of acceptance…
Descriptors: Foreign Countries, Electronic Learning, Distance Education, College Students
Wilson, Jonela Carmada Marisa; Kandege, Patrick; Edjoukou, Akadje Jean Roland; Teklu, Mussie Tesfay – Education and Information Technologies, 2021
The education system has been radically transformed by technological impetuses owed to the Fourth Industrial Revolution (4.0). Most recently, developing nations expedited smart education implementation to combat the negative effects COVID-19 has on education; thus, presenting managerial issues. A review of the literature on smart education shows…
Descriptors: Leadership, Human Resources, Technology Uses in Education, COVID-19
Oscar Carrera – ProQuest LLC, 2024
This correlational study investigated how secondary educators' engagement in online learning communities relates to their tech-enhanced instructional practices, a relationship that gained importance following the COVID-19 pandemic's acceleration of educational technology adoption. Through the lens of Keller's ARCS Model of Motivation (Keller,…
Descriptors: Electronic Learning, Secondary School Teachers, Teaching Methods, Technology Uses in Education
Zhao, Guoqing; Wang, Qiong; Wu, Lingxiang; Dong, Yan – Asia-Pacific Education Researcher, 2022
University students always suffer from technostress and experience burnout during technology-enhanced learning (TEL). Research revealing whether and how university support contributes to eliminate them lacks. This study aims to investigate the structural relationship among "university support" (i.e., "administration support"…
Descriptors: Student College Relationship, Anxiety, Technology Uses in Education, Burnout

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