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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
Qian Tian; Xudong Zheng – Journal of Computer Assisted Learning, 2024
Background: During the COVID-19 pandemic, online collaborative problem solving (online CPS) has become one of the most crucial learning methods to develop students' learning performance. However, it remains unclear of the effectiveness of the online CPS method on students' learning performance. Objectives: To explore the overall effect of online…
Descriptors: Electronic Learning, COVID-19, Pandemics, Teaching Methods
Chen, Xiuyu; Feng, Shihui – Journal of Computer Assisted Learning, 2023
Background: Video-based learning (VBL) is the learning process of acquiring defined knowledge, competence, and skills with the systematic use of video resources. Currently, the relationship between teaching presence and social presence in VBL is underexamined. Objectives: This study examined the relationships between social presence and teaching…
Descriptors: Teacher Student Relationship, Social Behavior, Electronic Learning, Video Technology
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
Marquès Puig, Joan Manuel; Daradoumis, Thanasis; Arguedas, Marta; Calvet Liñan, Laura – Journal of Computer Assisted Learning, 2022
Background: Recent research in online settings reports that supporting self-regulated learning (SRL) strategy use could lead to greater online academic success. A growing number of studies have started to investigate SRL supports in online environments recently, which indicates a great interest in this matter. Though several systems for automatic…
Descriptors: Cognitive Processes, Metacognition, Critical Thinking, Electronic Learning
Bissonnette, Steve; Boyer, Christian – Journal of Computer Assisted Learning, 2022
Tingir et al. (2017) concluded from their meta-analysis that the subject areas taught through mobile devices had significantly higher achievement scores (d = 0.48) than the ones taught with traditional teaching methods. Given the relatively high positive effect of mobile devices on student achievement, we carefully analysed the selected research…
Descriptors: Meta Analysis, Electronic Learning, Handheld Devices, Academic Achievement
Rocío Mínguez-Pardo; Javier Del Olmo-Muñoz; Sergio Tirado-Olivares; José Antonio González-Calero – Journal of Computer Assisted Learning, 2024
Background: The present study delves into the debate surrounding the effectiveness of homework, focusing specifically on the use of online homework in teaching decimal numbers to 5th and 6th grade primary school students. It highlights the challenges students face in representing and identifying decimal numbers, a concept that is often difficult…
Descriptors: Electronic Learning, Homework, Mathematics Instruction, Number Concepts
Eshuis, Elise H.; ter Vrugte, Judith; Anjewierden, Anjo; de Jong, Ton – Journal of Computer Assisted Learning, 2022
Background: Creating concept maps can help students overcome challenges of accurate knowledge monitoring and thus foster learning. However, students' knowledge often contains gaps and misconceptions, even after concept map creation. Theoretically, students could benefit from additional support, but it is unclear whether this might also be the case…
Descriptors: Reflection, Concept Mapping, Knowledge Representation, Instructional Effectiveness
Wang, Yang; Liu, Qingtang – Journal of Computer Assisted Learning, 2020
This study analysed the instructors' teaching presence of three courses conducted by an instructor to explore the effects of the instructors' online teaching presence on students' interactions and collaborative knowledge constructions. Content analysis, social network analysis, and lag sequential analysis were used to explore the mechanism of…
Descriptors: Online Courses, Teacher Student Relationship, Cooperative Learning, Electronic Learning
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
Zohre Mohammadi Zenouzagh; Wilfried Admiraal; Nadira Saab – Journal of Computer Assisted Learning, 2024
Background study: Although the number of computer-based instruction has increased drastically, the understanding of how design features of learning modality can affect learning remains incomplete. This partly stems from studies' heavy focus on modified output. Therefore, how interactive nature of computer-mediated learning feeds into learning is…
Descriptors: Computer Mediated Communication, Communication Skills, Self Control, Learner Engagement
Ucar, Hasan; Kumtepe, Alper Tolga – Journal of Computer Assisted Learning, 2020
This exploratory experimental study investigates the impact of motivational strategies based on the Attention, Relevance, Confidence, Satisfaction, and Volition (ARCS-V) model on online learners' academic performance, motivation, volition, and course interest. The research was conducted over an 11-week semester with 122 undergraduate online…
Descriptors: Motivation Techniques, Student Motivation, Online Courses, Electronic Learning
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
Lange, C.; Costley, J. – Journal of Computer Assisted Learning, 2018
Highly interactive and complex content within e-learning induces high levels of intrinsic load. Self-regulated effort represents one strategy that may help learners overcome such issues within e-learning. Using intrinsic load items representative of content complexity, germane load items representative of learning, and self-regulated effort items…
Descriptors: Metacognition, Electronic Learning, Independent Study, Correlation
Thai, Ngoc Thuy Thi; De Wever, Bram; Valcke, Martin – Journal of Computer Assisted Learning, 2020
This study compares four learning environments: face-to-face learning (F2F), fully e-learning (EL), blended learning (BL), and flipped classroom (FC) with respect to students' learning performance. Moreover, this present research studies changes in perceived flexibility, intrinsic motivation, self-efficacy beliefs of students, and the interaction…
Descriptors: Blended Learning, Teaching Methods, Comparative Analysis, Self Efficacy
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