Publication Date
In 2025 | 0 |
Since 2024 | 4 |
Since 2021 (last 5 years) | 7 |
Since 2016 (last 10 years) | 10 |
Since 2006 (last 20 years) | 16 |
Descriptor
Source
Journal of Computer Assisted… | 16 |
Author
Adela Descals | 1 |
Ahlqvist, J. | 1 |
Alexander Eitel | 1 |
Alexander Renkl | 1 |
Andreas Frey | 1 |
Anjewierden, Anjo | 1 |
Anquetil, Éric | 1 |
Aravena, R. | 1 |
Aron Fink | 1 |
Bremer, C. | 1 |
Chen, H.-L. | 1 |
More ▼ |
Publication Type
Journal Articles | 16 |
Reports - Research | 13 |
Reports - Descriptive | 2 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 5 |
Postsecondary Education | 5 |
High Schools | 3 |
Secondary Education | 3 |
Elementary Education | 2 |
Adult Education | 1 |
Grade 10 | 1 |
Grade 8 | 1 |
Grade 9 | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
More ▼ |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
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
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
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
Exploring Learner Motivation and Mobile-Assisted Peer Feedback in a Business English Speaking Course
Xu, Qi; Peng, Hongying – Journal of Computer Assisted Learning, 2022
Background: Peer-to-peer feedback exchanges have been recognized as crucial to language learning. While studies on peer feedback proliferate, little is known about whether and how peer feedback is affected by learners' motivational levels. Objectives: Situated in a mobile collaborative learning context, the current study examined how English as a…
Descriptors: Student Motivation, Electronic Learning, Handheld Devices, Peer Evaluation
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
Michinov, Nicolas; Anquetil, Éric; Michinov, Estelle – Journal of Computer Assisted Learning, 2020
Peer Instruction is an active learning method widely used in higher education, whereby students answer a series of questions twice, once before and once after peer discussion. There is an ongoing debate as to whether a collective feedback should be given after the students' initial answer, and if so, how the frequently observed group conformity…
Descriptors: Peer Teaching, Feedback (Response), Discussion, Active Learning
de Mooij, Susanne M. M.; Raijmakers, Maartje E. J.; Dumontheil, Iroise; Kirkham, Natasha Z.; van de Maas, Han L. J. – Journal of Computer Assisted Learning, 2021
While response time and accuracy indicate overall performance, their value in uncovering cognitive processes, underlying learning, is limited. A promising online measure, designed to track decision-making, is computer mouse tracking, where mouse attraction towards different locations may reflect the consideration of alternative response options.…
Descriptors: Error Patterns, Identification, Computer Peripherals, Computer Uses in Education
Magana, Alejandra J.; Serrano, Mayari I.; Rebello, N. Sanjay – Journal of Computer Assisted Learning, 2019
Virtual learning environments can now be enriched not only with visual and auditory information, but also with tactile and kinesthetic feedback. However, the way to successfully integrate haptic feedback on a multimodal learning environment is still unclear. This study aims to provide guidelines on how visuohaptic simulations can be implemented…
Descriptors: Sequential Learning, Student Development, Concept Formation, Electronic Learning
Yen, M.-H.; Chen, S.; Wang, C.-Y.; Chen, H.-L.; Hsu, Y.-S.; Liu, T.-C. – Journal of Computer Assisted Learning, 2018
This article develops a framework for self-regulated digital learning, which supports for self-regulated learning (SRL) in e-learning systems. The framework emphasizes 8 features: learning plan, records/e-portfolio and sharing, evaluation, human feedback, machine feedback, visualization of goals/procedures/concepts, scaffolding, and agents. Each…
Descriptors: Independent Study, Electronic Learning, Models, Online Courses
Uzunboylu, H.; Ozdamli, F. – Journal of Computer Assisted Learning, 2011
Successful integration of mobile learning (m-learning) technologies in education primarily demands that teachers' perception of such technologies should be determined. Therefore, the perceptions of teachers are of great significance. There is no available instrument that assesses teachers' perceptions of m-learning. Our research provided the first…
Descriptors: Electronic Learning, Feedback (Response), Test Validity, Measures (Individuals)
Gu, X.; Gu, F.; Laffey, J. M. – Journal of Computer Assisted Learning, 2011
The Life-long Learning Initiative seeks to fulfil a variety of learning needs for Shanghai citizens. Given the popularity of mobile devices in Shanghai, the ability to provide learning in informal settings through mobile devices is a key objective and challenge of the Initiative. In order to learn how to develop usable learning content for…
Descriptors: Foreign Countries, Handheld Devices, Mobility, Computer System Design
Bremer, C. – Journal of Computer Assisted Learning, 2012
The paper describes the procedure model AKUE, which aims at the improvement and assurance of quality and cost efficiency in the context of the introduction of e-learning and the development of digital learning material. AKUE divides the whole planning and implementation process into four different phases: analysis, conception, implementation, and…
Descriptors: Electronic Learning, Feedback (Response), Cost Effectiveness, Efficiency
Hall, L. O.; Soderstrom, T.; Ahlqvist, J.; Nilsson, T. – Journal of Computer Assisted Learning, 2011
This article is about collaborative learning with educational computer-assisted simulation (ECAS) in health care education. Previous research on training with a radiological virtual reality simulator has indicated positive effects on learning when compared to a more conventional alternative. Drawing upon the field of Computer-Supported…
Descriptors: Feedback (Response), Computer Simulation, Allied Health Occupations Education, Achievement Tests
Veletsianos, G. – Journal of Computer Assisted Learning, 2009
The possible benefits of agent expressiveness have been highlighted in previous literature; yet, the issue of verbal expressiveness has been left unexplored. I hypothesize that agent verbal expressiveness may improve the interaction between pedagogical agents and learners, ultimately enhancing learning outcomes. Evidence from a quasi-experimental…
Descriptors: Computer Assisted Instruction, Computer Software, Feedback (Response), Interaction
Previous Page | Next Page »
Pages: 1 | 2