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Shuling Yang; Carin Appleget – Journal of Digital Learning in Teacher Education, 2024
Guided by the Technology Acceptance Model, the researchers designed a Google Form survey to explore elementary preservice teachers' (PSTs') perceptions of using Generative AI (GenAI) as part of an authentic literacy methods course activity. Following the activity, responses to a qualitative survey were analyzed to learn about PSTs' experience of…
Descriptors: Artificial Intelligence, Educational Technology, Preservice Teachers, Elementary School Teachers
Chenwei Ma; Fang Wang; Yang Lv – Education and Information Technologies, 2024
Bullet screen technology enables instant interactive class communication and comments for teachers and students, even in large classes. This study explores the effect of a bullet-screen comment class on students' learning. It utilizes media and communication theory, expectation confirmation theory, and the technology acceptance model to present…
Descriptors: Foreign Countries, Educational Technology, Undergraduate Students, Correlation
Joseph Lobo; Francisco Gabriel Prevandos; Jem Cloyd Tanucan; Edi Setiawan – Journal of Learning for Development, 2024
Research shows that videoconferencing tools can be used for delivering high-quality instruction in real-time to a large number of students simultaneously during and even in the aftermath of the pandemic. However, there is a lack of data on how favourable it is among students, particularly in the Philippine setting, in the field of Physical…
Descriptors: Videoconferencing, Educational Technology, Physical Education, Student Attitudes
Abdulla Al Darayseh; Nazan Mersin – International Electronic Journal of Mathematics Education, 2025
This study investigates the perceptions of STEM teachers regarding the integration of generative artificial intelligence (AI) applications into classroom settings, using the extended Technology Acceptance Model (TAM) as the framework. The study examines self-efficacy, anxiety, perceived ease of use, expected benefits, attitudes, and behavioral…
Descriptors: Artificial Intelligence, STEM Education, Science Teachers, Mathematics Teachers
Tao, Da; Fu, Pei; Wang, Yunhui; Zhang, Tingru; Qu, Xingda – Interactive Learning Environments, 2022
In spite of the proliferation of Massive Open Online Courses (MOOCs) in higher education, factors influencing user acceptance of MOOCs are not well understood. This study is intended to investigate key characteristics of user acceptance from interface design (i.e. usability), content quality (i.e. perceived quality), and emotional arousal (i.e.…
Descriptors: MOOCs, Computer Attitudes, Educational Technology, Technology Uses in Education
Barrett, Alex; Pack, Austin; Guo, Yajing; Wang, Ningjuan – Interactive Learning Environments, 2023
Virtual reality is a rapidly developing technology and its applications are being explored in a variety of educational fields. While an increasing amount of attention is being paid to VR as a language learning tool, there remains less research on using VR with languages other than English. Adapting the Technology Acceptance Model, this study…
Descriptors: Second Language Learning, Chinese, Educational Technology, Technology Uses in Education
Ohanu, Ifeanyi B.; Shodipe, Taiwo O.; Ohanu, Chinenye M-G.; Anene-Okeakwa, Josephine E. – E-Learning and Digital Media, 2023
There are declines in learning outcomes despite teacher's efforts to attain the lesson goals and objectives. The application of technology in education paved way for constructive teaching and learning, which made the evaluative processes convenient. Hence, this study investigated system quality, technology acceptance model and theory of planned…
Descriptors: Educational Technology, Technology Uses in Education, Blended Learning, College Students
Nja, Cecilia Obi; Idiege, Kimson Joseph; Uwe, Uduak Edet; Meremikwu, Anne Ndidi; Ekon, Esther Etop; Erim, Costly Manyo; Ukah, Julius Ukah; Eyo, Eneyo Okon; Anari, Mary Ideba; Cornelius-Ukpepi, Bernedette Umalili – Smart Learning Environments, 2023
This study investigated the factors influencing science teachers' 'Artificial Intelligence' (AI) utilization by using the 'Technology Acceptance Model' (TAM). The factors investigated alongside TAM variables were teachers' data like; age, sex, and residence type. TAM items that were correlated in this study included; self-esteem, stress and…
Descriptors: Science Teachers, Educational Technology, Technology Integration, Artificial Intelligence
Ashrafi, Amir; Zareravasan, Ahad; Rabiee Savoji, Sogol; Amani, Masoumeh – Interactive Learning Environments, 2022
In the last decades, universities and higher education institutes have widely employed a learning management system (LMS) to monitor and manage electronic learning and teaching. Contrary to the significant role of LMS in educational settings, most research has focused on initial acceptance, and few attempts have been made to investigate factors…
Descriptors: College Students, Intention, Learning Management Systems, Educational Technology
Adopting and Using Geospatial Technologies for Teaching Geography in Latin American Higher Education
Cascante-Campos, Alejandro – Journal of Geography in Higher Education, 2023
The article presents the results of a regional study analysing the factors that predict Latin American faculty adoption and use of geospatial technologies (GST) for teaching geography. The research involved an online survey with 337 participants from 17 countries, who answered a set of items adapted from the Unified Theory of Acceptance and Use of…
Descriptors: Foreign Countries, Higher Education, Geography Instruction, Geographic Information Systems
Rokhman, Fathur; Mukhibad, Hasan; Bagas Hapsoro, Bayu; Nurkhin, Ahmad – Cogent Education, 2022
The Covid-19 pandemic makes the government adjust the learning system from face-to-face to e-learning amid restrictions on mass social contact. We develop the Updated Delone and McLean information systems success (D&M ISS) model by incorporating external factors. The newly added constructs include student capability, teacher capability, and…
Descriptors: COVID-19, Pandemics, Educational Technology, Electronic Learning
Nguyen Thi Thao Ho; Muhammad Ridhuan Tony Lim Abdullah; Hairuzila Bt Idrus; Subarna Sivapalan; Hiep-Hung Pham; Viet-Hung Dinh; Huyen Khanh Pham; Linh Thi My Nguyen – SAGE Open, 2023
This triangulation mixed methods study employed the Technology Acceptance Model (TAM) to investigate the factors affecting continuance intention toward Coursera MOOCs blended learning (CMBL) with undergraduate students at a Vietnamese private higher education institution (HEI). IBM AMOS version 24 was employed, with which Confirmatory Factor…
Descriptors: MOOCs, Educational Technology, Blended Learning, Higher Education
Perera, R.H.A.T.; Abeysekera, Nalin – Asian Association of Open Universities Journal, 2022
Purpose: The purpose of this paper is to identify the factors affecting learners' perception of e-learning during the Coronavirus-2019 (COVID-19) pandemic. Furthermore, it investigates mechanisms that enhance students' engagement in e-learning, especially under the constraints created by the pandemic. Design/methodology/approach: Quantitative…
Descriptors: Student Attitudes, Electronic Learning, Educational Technology, School Closing
Callimaci, Antonello; Fortin, Anne – Journal of Further and Higher Education, 2022
Use of educational technology has escalated in recent times. This study surveyed teachers in the business school of a Canadian university after it quickly pivoted to online delivery of all classes after the initial jolt of the COVID-19 pandemic. The purpose of the survey was to investigate the antecedents of the teachers' intention to use…
Descriptors: Educational Technology, Technology Uses in Education, COVID-19, Pandemics
Nassr, Rasheed Mohammad; Alkhnbashi, Omer S.; Aldossary, Alia Ahmed; Bamasoud, Doaa M.; Amiruddin, Ungku Nurshakira binti Ungku – Journal of Educators Online, 2023
Lockdowns due to the COVID-19 pandemic have forced universities to migrate entirely to eLearning to deliver classes. Students have found themselves using either synchronous, asynchronous, or mixed mode learning methods based on their universities' preferences. Many studies that investigated difficulties during the lockdown have been mainly…
Descriptors: Foreign Countries, COVID-19, Pandemics, School Closing