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Chengliang Wang; Xiaojiao Chen; Zhebing Hu; Sheng Jin; Xiaoqing Gu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Computer Uses in Education
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Jelena Jovanovic; Dragan Gaševic; Lixiang Yan; Graham Baker; Andrew Murray; Danijela Gasevic – Journal of Computer Assisted Learning, 2024
Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable…
Descriptors: Psychological Patterns, Networks, Profiles, Learning Processes
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Xin Tang; Zhiqiang Yuan; Shaojun Qu – Journal of Computer Assisted Learning, 2025
Background: Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors of society and is anticipated to revolutionise the educational landscape, especially in the…
Descriptors: Influences, College Students, Student Behavior, Intention
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Irum Alvi; Hager Khechine – Journal of Computer Assisted Learning, 2025
Background: Collaborative learning, which emphasises cooperative group techniques, intersects with the evolving role of social media as a tool. Understanding how cultural values influence these dynamics is crucial for effectively integrating and utilising social media into collaborative learning environments. Objective: This research aims to…
Descriptors: Foreign Countries, College Students, Student Attitudes, Engineering Education
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Hui-Tzu Hsu; Chih-Cheng Lin – Journal of Computer Assisted Learning, 2024
Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…
Descriptors: Intention, Vocabulary Development, Handheld Devices, College Students
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Marco Rüth; Maria Jansen; Kai Kaspar – Journal of Computer Assisted Learning, 2024
Background: Online exams have become a more common form of assessment at universities due to the COVID-19 pandemic. However, cheating behaviour in online exams is widespread and threatens exam validity as well as student learning and well-being. Objective: To better understand the role of university students' needs, conceptions and reasons…
Descriptors: Foreign Countries, College Students, Computer Assisted Testing, Cheating
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Ranellucci, John; Rosenberg, Joshua M.; Poitras, Eric G. – Journal of Computer Assisted Learning, 2020
Understanding how prepared teachers are to use technology to enhance their teaching can assist researchers to support them better, yet the theoretical basis for understanding teachers' self-beliefs is in need of stronger empirical support. The first objective of this study was to replicate and extend prior research that empirically tested portions…
Descriptors: Preservice Teachers, Technology Uses in Education, Computer Attitudes, Adoption (Ideas)
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Menabò, Laura; Sansavini, Alessandra; Brighi, Antonella; Skrzypiec, Grace; Guarini, Annalisa – Journal of Computer Assisted Learning, 2021
Background: The rapid spread of COVID-19 forced many countries to adopt severe containment measures, transferring all didactic activities into virtual environments. However, the integration of technology in teaching may present difficulties, especially in some countries, such as Italy. Objectives: The present study analyzed how the two main…
Descriptors: Technology Integration, Intention, Adoption (Ideas), Electronic Learning
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Lin, Chih-Chung; Barrett, Neil E.; Liu, Gi-Zen – Journal of Computer Assisted Learning, 2021
Context-aware ubiquitous learning (CAUL) technology provides language learners with interactive learning environments and has been found to increase learning effectiveness and self-efficacy due to student interaction, discussion and evaluation of the entire learning process. This study used a mobile-based ubiquitous learning system combined with a…
Descriptors: English (Second Language), Handheld Devices, Cooperative Learning, Second Language Learning
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Dindar, Muhterem; Suorsa, Anna; Hermes, Jan; Karppinen, Pasi; Näykki, Piia – Journal of Computer Assisted Learning, 2021
COVID-19 pandemic has caused a massive transformation in K-12 settings towards online education. It is important to explore the factors that facilitate online teaching technology adoption of teachers during the pandemic. The aim of this study was to compare Learning Management System (LMS) acceptance of Finnish K-12 teachers who have been using a…
Descriptors: Technology Integration, Elementary School Teachers, Secondary School Teachers, Expectation
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Ding, Lu; Er, Erkan – Journal of Computer Assisted Learning, 2018
Research has noted the effectiveness of online tools (e.g., discussion boards) for supporting help seeking among class members. However, help seeking is not necessarily warranted via online learning tools because some factors (e.g., low Internet self-efficacy) may influence students' intention to use them. This study aims to identify the…
Descriptors: College Students, Help Seeking, Computer Uses in Education, Influences