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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
Min Young Doo; Meina Zhu – Journal of Computer Assisted Learning, 2024
Background: Online learning has become more prevalent over the past three decades, especially during the COVID-19 pandemic. Educators and scholars have increasingly emphasized the significance of self-directed learning (SDL) on successful learning outcomes in online learning environments. Objectives: The purpose of this study was to synthesize the…
Descriptors: Electronic Learning, Independent Study, Virtual Classrooms, Academic Achievement
Yuhui Jing; Chengliang Wang; Zhaoyi Chen; Shusheng Shen; Rustam Shadiev – Journal of Computer Assisted Learning, 2024
Background Study: Technology-supported learning environments, act as significant observational and enabling indicators for evaluating and encouraging the digital revolution of education, are of vital importance in current educational research. Keeping track of the dynamics of technology-supported learning environment research allows for the…
Descriptors: Educational Technology, Technology Uses in Education, Educational Environment, Educational Research
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
Yuko Suzuki; Fridolin Wild; Eileen Scanlon – Journal of Computer Assisted Learning, 2024
Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non-invasive biometric sensors, unbiased real-time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject…
Descriptors: Computer Simulation, Cognitive Processes, Difficulty Level, Physiology
Sun, Chunmei; Hwang, Gwo-Jen; Yin, Zhaoyi; Wang, Zhonghou; Wang, Zhuo – Journal of Computer Assisted Learning, 2023
Background: Social annotation (SA) allows users to collaboratively highlight important texts, make comments and discuss with each other on the same online document. This would not only accelerate and deepen learners' cognitive understanding of information, but also help build a sense of rapport, which is critical especially because of the…
Descriptors: Trend Analysis, Computer Mediated Communication, Cooperative Learning, Documentation
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
Jia Li; Novera Roihan; Matthew McGravey – Journal of Computer Assisted Learning, 2025
Background: Online learning has become a popular form of education. Prior to the COVID-19 pandemic, online learning was mainly associated with higher education, with an incremental growth at the K-12 level. The pandemic changed this situation rapidly. Online instruction has been increasingly integrated into secondary schools and has significantly…
Descriptors: High School Students, Student Attitudes, Electronic Learning, Student Experience
Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
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
Theelen, Hanneke; van Breukelen, Dave H. J. – Journal of Computer Assisted Learning, 2022
Background: Since about 2010 e-learning has been embedded in educational practice and has become, surely due to the COVID-19 pandemic, increasingly important. Objectives: Although much has been written about e-learning, little is known about crucial didactic and pedagogical design principles for e-learning. This review tried to fill that gap.…
Descriptors: Instructional Design, Electronic Learning, Higher Education, COVID-19
Liu, Caihua; Zowghi, Didar; Kearney, Matthew; Bano, Muneera – Journal of Computer Assisted Learning, 2021
Recent years have seen a growing call for inquiry-based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology-supported inquiry-based learning (mIBL) in secondary science education. More evidence-based, nuanced…
Descriptors: Active Learning, Inquiry, Electronic Learning, Technology Integration
Akturk, Ahmet Oguz – Journal of Computer Assisted Learning, 2022
Background: "Journal of Computer Assisted Learning" ("JCAL"), which started its publication life in 1985, is a leading international journal in the field of computer and instructional technologies and celebrated its 35th anniversary in 2020. Objectives: This study aims to provide a bibliometric overview of leading publication…
Descriptors: Periodicals, Bibliometrics, Citations (References), Educational Technology
Savva, Marilena; Higgins, Steve; Beckmann, Nadin – Journal of Computer Assisted Learning, 2022
Background: The array of availability of diverse digital reading applications, the mixed results emerging from small-scale experimental studies, as well as the long-standing tradition and range of known positive developmental outcomes gained from adult-child storybook reading warrant an investigation into electronic storybooks (e-books) by…
Descriptors: Preschool Children, Grade 1, Grade 2, Story Reading
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