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Journal of Computer Assisted…961
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Showing 16 to 30 of 961 results Save | Export
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Wang, Wei-Sheng; Cheng, Yu-Ping; Lee, Hsin-Yu; Lin, Chia-Ju; Huang, Yueh-Min – Journal of Computer Assisted Learning, 2023
Background: Benefited from advances in technology, virtual reality (VR) has been widely applied to learning content in operational training as well as hands-on courses. However, most current studies tend to evaluate learning effectiveness in this application, and few were focused on how learners can be benefited from transferring the knowledge…
Descriptors: Computer Simulation, Learning, Experiential Learning, Anxiety
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Frank Wehrmann; Raphael Zender – Journal of Computer Assisted Learning, 2025
Background: The role of virtual reality (VR) in education is increasing, which raises questions about VR learning in multi-user settings. While collaborative VR learning, characterised by shared goals and low division of labour, is well-researched, cooperative VR learning, which emphasises role differentiation and task interdependence, remains…
Descriptors: Cooperative Learning, Computer Simulation, Learning Processes, Individualized Instruction
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Lanqin Zheng; Yunchao Fan; Zichen Huang; Lei Gao – Journal of Computer Assisted Learning, 2024
Background: Online collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning. Objectives: The study compared the impacts of the three learning approaches on collaborative knowledge building,…
Descriptors: Cooperative Learning, Electronic Learning, College Students, Learning Strategies
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Evi-Colombo, Alessia; Cattaneo, Alberto; Bétrancourt, Mireille – Journal of Computer Assisted Learning, 2023
Background: While the use of digital technologies in collaborative design tasks have gained acceptance amongst educational researchers and instructors, few studies have analysed the application of video-supported collaborative learning-by-design (VSC-LBD) in the authentic setting of professional education and training. Objectives: This study on…
Descriptors: Knowledge Level, College Students, Nursing Education, Instructional Effectiveness
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Jyun-Chen Chen; Chia-Yu Liu – Journal of Computer Assisted Learning, 2024
Background: Society requires individuals to have the ability to synthesise knowledge from diverse sources, typically acquired through science, technology, engineering, and mathematics (STEM) education. Objectives and Methods: We utilised a moderately rigorous design to investigate the effects of combining a hands-on STEM activity with active…
Descriptors: Knowledge Level, Teaching Methods, Flipped Classroom, Academic Achievement
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Asma Almusharraf; Hassan Saleh Mahdi; Haifa Al-Nofaie; Amal Aljasser – Journal of Computer Assisted Learning, 2024
Background study: Computer-assisted pronunciation training (CAPT) has emerged as an important resource for second language (L2) pronunciation learning. CAPT offers diverse opportunities for learners to master L2 pronunciation with the help of authentic learning activities in multiple contexts. Objectives: This study presents a comprehensive…
Descriptors: Computer Assisted Instruction, Pronunciation Instruction, Second Language Learning, Second Language Instruction
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Hsu, Keng-Chih; Liu, Gi-Zen – Journal of Computer Assisted Learning, 2023
Background: Given the advancement of mobile and sensing technology, the incorporation of augmented reality (AR) in context-aware ubiquitous learning (CAUL) has offered significant benefits to oral communication development in foreign language learning. Although a great number of studies have been dedicated in this field, only a little research was…
Descriptors: Computer Simulation, Educational Technology, Best Practices, Computer Uses in Education
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Ahmed Tlili; Soheil Salha; Juan Garzón; Mouna Denden; Kinshuk; Saida Affouneh; Daniel Burgos – Journal of Computer Assisted Learning, 2024
Background Study: Several meta-analysis studies have investigated the effects of mobile learning on learning performance. However, limited attention has been paid to pedagogy in mobile learning, making quantitative evidence of the effects of pedagogical approaches on learning performance in mobile learning scarce. Filling this gap can therefore…
Descriptors: Teaching Methods, Instructional Effectiveness, Electronic Learning, Student Experience
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Wannapon Suraworachet; Qi Zhou; Mutlu Cukurova – Journal of Computer Assisted Learning, 2025
Background: Many researchers work on the design and development of multimodal collaboration support systems with AI, yet very few of these systems are mature enough to provide actionable feedback to students in real-world settings. Therefore, a notable gap exists in the literature regarding students' perceptions of such systems and the feedback…
Descriptors: Graduate Students, Student Attitudes, Artificial Intelligence, Cooperative Learning
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Wilk Oliveira; Pasqueline Dantas Scaico; Juho Hamari; Zhaoxing Li; Lei Shi – Journal of Computer Assisted Learning, 2025
Background: Gamification has been used in recent years to enhance the student experience in educational environments and to help students achieve an optimal experience. However, there is limited empirical evidence of the effects of gamification on specific psychological experiences, such as the students' flow experience, which is a highly…
Descriptors: Gamification, Learning Management Systems, Game Based Learning, Student Experience
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Tanya Chichekian; Joel Trudeau; Tawfiq Jawhar; Dylan Corliss – Journal of Computer Assisted Learning, 2024
Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K-12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to…
Descriptors: College Students, Computation, Thinking Skills, Transfer of Training
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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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Esnaashari, Shadi; Gardner, Lesley A.; Arthanari, Tiru S.; Rehm, Michael – Journal of Computer Assisted Learning, 2023
Background: It is vital to understand students' Self-Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable-oriented approach. Moreover, little has been known about the unfolding process of students' SRL…
Descriptors: Metacognition, Student Attitudes, Learning Strategies, Questionnaires
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Peijian Paul Sun; Zeqi Ren; Xian Zhao – Journal of Computer Assisted Learning, 2025
Background: Student engagement has been conceptualised and operationalised in various learning environments. However, there is currently a lack of established scales to measure student engagement in synchronous online learning. One possible reason is the existence of the conceptual and structural ambiguity regarding student engagement. Objective:…
Descriptors: Student Participation, Second Language Learning, Synchronous Communication, Electronic Learning
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Tornike Giorgashvili; Ioana Jivet; Cordula Artelt; Daniel Biedermann; Daniel Bengs; Frank Goldhammer; Carolin Hahnel; Julia Mendzheritskaya; Julia Mordel; Monica Onofrei; Marc Winter; Ilka Wolter; Holger Horz; Hendrik Drachsler – Journal of Computer Assisted Learning, 2025
Background: Learning analytics dashboards (LAD) have been developed as feedback tools to help students self-regulate their learning (SRL) by using the large amounts of data generated by online learning platforms. Despite extensive research on LAD design, there remains a gap in understanding how learners make sense of information visualised on LADs…
Descriptors: Field Studies, Student Reaction, Feedback (Response), Learning Analytics
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