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Showing 1 to 15 of 23 results Save | Export
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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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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
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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
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Ignacio Máñez; Noemi Skrobiszewska; Adela Descals; María José Cantero; Raquel Cerdán; Óscar Fernando García; Rafael García-Ros – Journal of Computer Assisted Learning, 2024
Background: Delivering effective feedback to large groups of students represents a challenge for the academic staff at universities. Research suggests that undergraduate students often ignore the Elaborated Feedback (EF) received via digital learning environments. This may be because instructors provide feedback in written format instead of using…
Descriptors: Feedback (Response), Audiovisual Aids, Higher Education, College Students
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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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Shuang Li; Jingxi Chen; Sizhuo Liu – Journal of Computer Assisted Learning, 2024
Background: Enhancing the effectiveness of online learning has become a key challenge with regard to the ability of blended learning to reach its full potential. However, mechanisms by which students' self-regulated learning (SRL) skills influence their online learning engagement in blended learning and subsequent learning achievement have yet to…
Descriptors: Blended Learning, Elementary Secondary Education, Self Management, Self Evaluation (Individuals)
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Okan Bulut; Guher Gorgun; Seyma Nur Yildirim-Erbasli – Journal of Computer Assisted Learning, 2025
Background: Research shows that how formative assessments are operationalized plays a crucial role in shaping their engagement with formative assessments, thereby impacting their effectiveness in predicting academic achievement. Mandatory assessments can ensure consistent student participation, leading to better tracking of learning progress.…
Descriptors: Formative Evaluation, Academic Achievement, Student Participation, Learning Processes
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Bacca-Acosta, Jorge; Fabregat, Ramon; Baldiris, Silvia; Kinshuk; Guevara, Juan – Journal of Computer Assisted Learning, 2022
Background: Mobile-based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile-based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives: This study aims to identify…
Descriptors: Academic Achievement, Electronic Learning, Handheld Devices, Computer Assisted Testing
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Özüdogru, Melike – Journal of Computer Assisted Learning, 2022
Background: There is a scarcity of studies on online flipped learning in teacher education classes. Many studies have found that student learning is improved in flipped learning environments; however, this is still an open question. Much of the literature employs quantitative methods to reveal the effect of flipped learning on certain variables…
Descriptors: Student Experience, Preservice Teachers, Electronic Learning, Flipped Classroom
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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
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Teresa M. Ober; Ying Cheng; Matthew F. Carter; Cheng Liu – Journal of Computer Assisted Learning, 2024
Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms.…
Descriptors: Academic Achievement, Feedback (Response), Student Behavior, At Risk Students
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Joshua Weidlich; Aron Fink; Ioana Jivet; Jane Yau; Tornike Giorgashvili; Hendrik Drachsler; Andreas Frey – Journal of Computer Assisted Learning, 2024
Background: Developments in educational technology and learning analytics make it possible to automatically formulate and deploy personalized formative feedback to learners at scale. However, to be effective, the motivational and emotional impacts of such automated and personalized feedback need to be considered. The literature on feedback…
Descriptors: Emotional Response, Student Motivation, Feedback (Response), Automation
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Han, Feifei; Pardo, Abelardo; Ellis, Robert A. – Journal of Computer Assisted Learning, 2020
This study examines the extent to which the learning orientations identified by student self-reports and the observation of their online learning events were related to each other and to their academic performance. The participants were 322 first-year engineering undergraduates, who were enrolled in a blended course. Using students' self-report on…
Descriptors: College Students, Electronic Learning, Blended Learning, Curriculum Design
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Katharina Alexandra Whalen; Alexander Renkl; Alexander Eitel; Inga Glogger-Frey – Journal of Computer Assisted Learning, 2024
Background: Students often show unfavourable attribution: they attribute poor school performance to stable factors such as lack of ability and good school performance to variable factors such as effort. However, attribution can be influenced by individualized digital re-attributional feedback leading to positive motivational effects and higher…
Descriptors: Feedback (Response), Computer Mediated Communication, Secondary School Mathematics, Student Motivation
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Guo, Pengyue; Saab, Nadira; Wu, Lin; Admiraal, Wilfried – Journal of Computer Assisted Learning, 2021
Project-based learning (PjBL) engages students in knowledge acquisition, application, and construction through artefact development. Based on the Community of Inquiry framework, this study characterized college students' social and cognitive presences in online PjBL and examined how presence was related to their academic performance. Twenty-four…
Descriptors: Communities of Practice, Inquiry, Active Learning, Electronic Learning
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