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Ho Young Yoon; Seokmin Kang; Sungyeun Kim – Journal of Computer Assisted Learning, 2024
Background: Research into enhancing the effectiveness of information delivery in asynchronous video lectures remains sparse. This study analyzes the nonverbal teaching behaviours in asynchronous online videos, drawing comparisons between pre-service and in-service teachers (ITs). Objectives: This research primarily aims to juxtapose the nonverbal…
Descriptors: Asynchronous Communication, Video Technology, Lecture Method, Nonverbal Communication
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Lanqin Zheng; Zichen Huang; Lei Gao; Yunchao Fan – Journal of Computer Assisted Learning, 2025
Background: Online collaborative learning has been broadly applied in the field of higher education. Nevertheless, not all types of collaborative learning can produce the desired learning results. Objectives: To facilitate online collaborative learning, the present study proposed an innovative artificial intelligence-enabled group cognitive…
Descriptors: Artificial Intelligence, Technology Uses in Education, Electronic Learning, Online Courses
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Robert F. Siegle; Scotty D. Craig – Journal of Computer Assisted Learning, 2024
Background: The voices virtual on-screen characters use has been shown to impact learning and perception outcomes. Recent replication research on these voices showed that synthetic voices were not a detriment if produced by a high-quality engine with clear articulation. The current manuscript examines previous accent research that utilized now…
Descriptors: Acoustics, Artificial Intelligence, Electronic Learning, Quality Assurance
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Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
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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
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Gerti Pishtari; María Jesús Rodríguez-Triana; Luis P. Prieto; Adolfo Ruiz-Calleja; Terje Väljataga – Journal of Computer Assisted Learning, 2024
Background: In the field of Learning Design, it is common that researchers analyse manually design artefacts created by practitioners, using pedagogically-grounded approaches (e.g., Bloom's Taxonomy), both to understand and later to support practitioners' design practices. Automatizing these high-level pedagogically-grounded analyses would enable…
Descriptors: Electronic Learning, Instructional Design, Active Learning, Inquiry
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Hui-Tzu Chang; Chia-Yu Lin – Journal of Computer Assisted Learning, 2024
Background: Numerous higher education institutions worldwide have adopted English-language-medium computer science courses and integrated online problem-solving competitions to bridge gaps in theory and practice (Alhamami "Education and Information Technologies," 2021; 26: 6549-6562). Objectives: This study aimed to investigate the…
Descriptors: Artificial Intelligence, Instructional Improvement, Problem Solving, Competition
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Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
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Weipeng Yang; Xinyun Hu; Ibrahim H. Yeter; Jiahong Su; Yuqin Yang; John Chi-Kin Lee – Journal of Computer Assisted Learning, 2024
Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Digital Literacy
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Lahza, Hatim; Khosravi, Hassan; Demartini, Gianluca – Journal of Computer Assisted Learning, 2023
Background: The use of crowdsourcing in a pedagogically supported form to partner with learners in developing novel content is emerging as a viable approach for engaging students in higher-order learning at scale. However, how students behave in this form of crowdsourcing, referred to as learnersourcing, is still insufficiently explored.…
Descriptors: Learning Analytics, Learning Strategies, Electronic Learning, Independent Study
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Flor, Michael; Andrews-Todd, Jessica – Journal of Computer Assisted Learning, 2022
Background: Collaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology-enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory-driven measurements that are usually carried out…
Descriptors: Automation, Documentation, Cooperative Learning, Teamwork
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Yu Cui; Lingjie Tang; Fang Fang – Journal of Computer Assisted Learning, 2025
Background Study: With the rapid transition to remote learning necessitated by the closure of traditional educational infrastructures globally, the arena of informal digital learning of English (IDLE) has received much attention, particularly among English as a Foreign Language (EFL) learners in China. Objective: This study explores how…
Descriptors: Electronic Learning, Artificial Intelligence, Predictor Variables, Informal Education
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Fangzhou Jin; Xiangmei Peng; Lanfang Sun; Zicong Song; Keyi Zhou; Chin-Hsi Lin – Journal of Computer Assisted Learning, 2025
Background: There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle. Objectives: Guided by community of practice, this paper examines the integration of GenAI into an…
Descriptors: Artificial Intelligence, Communities of Practice, Technology Uses in Education, Experienced Teachers
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Wafa Mohammed Aldighrir; Fatima's Mohamed Asiri – Journal of Computer Assisted Learning, 2025
Background: As educational institutions increasingly operate as multicultural hubs, leaders must navigate the complexities of cultural differences, language barriers and diverse learning styles in digital environments. These challenges are amplified by the lack of non-verbal cues and the asynchronous nature of online communication, which can lead…
Descriptors: Foreign Countries, Test Construction, Measures (Individuals), Test Validity
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Krouska, Akrivi; Troussas, Christos; Virvou, Maria – Journal of Computer Assisted Learning, 2019
Social networks have intruded in human life by providing new technological innovations in a range of fields, including the education. The use of social networks in education has the potential to extend e-learning and to introduce new forms of tutoring, communication, and collaboration between students and instructors. Thus, e-learning is the…
Descriptors: Social Networks, Guidelines, Electronic Learning, Teacher Student Relationship
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