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Phil Seok Oh; Gyeong-Geon Lee – Science & Education, 2025
How and why science education scholars and practitioners might use artificial intelligence (AI) in the classroom has been a controversial agenda for decades. ChatGPT, a state-of-the-art (SOTA) AI released in November 2022, has attracted global interest for its exceptionally high performance in generating human-like natural language answers to…
Descriptors: Science Education, Artificial Intelligence, Cognitive Processes, Affective Behavior
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Wang, Kai – International Review of Research in Open and Distributed Learning, 2023
This study incorporated the technology acceptance model (TAM) and theory of planned behavior (TPB) to interpret students' perception of MOOCs. This study was based on a survey questionnaire; all 525 respondents were undergraduates in China. A five-point Likert scale was used to collect data in order to measure relationships among the constructs of…
Descriptors: Foreign Countries, Undergraduate Students, MOOCs, Intention
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Faith Sylvester Orim; Usani Joseph Ofem; Imelda Barong Edam-Agbor; Nsan Njar Nsan; John Arikpo Okri; Patience Ekpang; Blessing Ogunjimi; Isu Michael Egbe; James Omaji Ukatu; Cecilia Undie Angrey; Moses Agba Undie; Oluwaseun Akin-Fakorede; Dymphina Abua; Peace Asukwo – Discover Education, 2025
The current educational landscape is flooded with new technological tools, especially with the arrival of artificial intelligence, which is applicable at all levels of instructional practices. However, the application of technology in assessment in higher education has been understudied. This study focused on filling this research gap by examining…
Descriptors: Higher Education, College Faculty, Computer Assisted Testing, Technology Integration
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Candela, Pablo P.; Gumbo, Mishack T.; Mapotse, Tomé A. – International Journal of Technology and Design Education, 2022
This study is about the adaptation of the Attitude Behavioural Scale (ABS) section of a Pupils' Attitudes Towards Technology (PATT) instrument for the Omani context. This study is a mixed methods research design and it consists of three phases. Phase 1 of the study is reported in this article. Phase 1 started with the selection of the PATT-USA as…
Descriptors: Attitude Measures, Foreign Countries, Computer Attitudes, Technology
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Feng Zhang; Gege Li; Heng Luo – Educational Technology & Society, 2025
With the development of virtual reality technology, 3D multi-user virtual environments (MUVEs) have attracted increasing research attention and are thought to bring many learning benefits in higher education. However, the widespread and sustained application of MUVEs in higher education lies in learners' intention to use them, but the mechanism…
Descriptors: Student Attitudes, Intention, College Students, Educational Technology
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Granit Baca; Genc Zhushi – Higher Education, Skills and Work-based Learning, 2025
Purpose: This study aims to examine the integration of AI in student engagement and its benefits in the learning environment. Design/methodology/approach: The study employed a quantitative research method, analyzing data from a sample of 720 students. The econometric data analysis used the structural equation modeling (SEM) technique. Findings:…
Descriptors: Artificial Intelligence, Technology Integration, Technology Uses in Education, Higher Education
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Oluwanife Segun Falebita; Petrus Jacobus Kok – Journal for STEM Education Research, 2025
This study investigates the relationship between undergraduates' technological readiness, self-efficacy, attitude, and usage of artificial intelligence (AI) tools. The study leverages the technology acceptance model (TAM) to explore the relationships among the study's variables. The study's participants are 176 undergraduate students from a public…
Descriptors: Artificial Intelligence, Technology Uses in Education, Structural Equation Models, Undergraduate Students
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Deliang Wang; Cunling Bian; Gaowei Chen – British Journal of Educational Technology, 2024
Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom…
Descriptors: Artificial Intelligence, Dialogs (Language), Discourse Analysis, Trust (Psychology)
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Khlaif, Zuheir N.; Sanmugam, Mageswaran; Ayyoub, Abedulkarim – Asia-Pacific Education Researcher, 2023
With rapid development of emerging technologies, teachers have been required to integrate mobile technology into their practices to improve learning outcomes. However, teachers have been reluctant to integrate technology into teaching because of technostress. Many studies have investigated the reasons and consequences of technostress in different…
Descriptors: Intention, Handheld Devices, Technology Integration, Computer Attitudes
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Jose Belda-Medina – Contemporary Educational Technology, 2025
This study investigates the impact of augmented reality (AR) on vocabulary and content learning, as well as attitudes, in a content and language integrated learning (CLIL) setting. The research, based on convenience sampling, involved 162 secondary education students from three schools, divided into an experimental group (EG) and a control group…
Descriptors: Content and Language Integrated Learning, Simulated Environment, Vocabulary Development, Language Acquisition
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Noble, Sean M.; Saville, Jason D.; Foster, Lori L. – International Journal of Educational Technology in Higher Education, 2022
Post-secondary institutions are investing in and utilizing virtual reality (VR) for many educational purposes, including as a discretionary learning tool. Institutions such as vocational schools, community colleges, and universities need to understand what psychological factors drive students' acceptance of VR for learning in discretionary…
Descriptors: Computer Simulation, Video Technology, Computer Attitudes, Adoption (Ideas)
Yazmin Muñiz de la Garza – ProQuest LLC, 2021
Once teachers begin their professional journey it is expected that they know how to integrate technology effectively. Therefore, exploring the experiences that influence their self-efficacy become an important aspect in which to inquire. The purpose of the study was to examine the self-efficacy of pre-service teachers related to the integration of…
Descriptors: Preservice Teachers, Student Attitudes, Self Efficacy, Technology Integration
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Balouchi, Shima; Samad, Arshad Abdul – Education and Information Technologies, 2021
There is a wealth of research investigating language learners' adoption of digital technology in higher education. To date, however, research primarily focuses on the implementation of technology in formal learning either inside or outside of the classroom, and the associations between factors influencing learners' informal English learning in…
Descriptors: Second Language Learning, English (Second Language), Informal Education, Intention
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Kaya, Suat – Research in Pedagogy, 2021
This research was conducted in an attempt to examine online learning satisfaction (OLS) level of the pre-service teachers and the influence of antecedents including computer anxiety (CA), internet anxiety (IA), online course anxiety (OCA), internet self-efficacy (ISE) and transactions including learner-instructor interaction (LII), learner-content…
Descriptors: Predictor Variables, Electronic Learning, Student Satisfaction, Preservice Teachers
Sharifa J. Simmons – ProQuest LLC, 2021
The digital age is reshaping learning and instruction and encouraging educational technology advances within higher education institutions. However, online faculty are not integrating technology into their classes despite the technology related professional development they receive. The purpose of this quantitative study was to determine if a…
Descriptors: Educational Technology, Online Courses, Technology Integration, Self Efficacy
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