Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 6 |
Since 2016 (last 10 years) | 8 |
Descriptor
Source
Education and Information… | 3 |
Journal of Computer Assisted… | 2 |
Canadian Journal of Learning… | 1 |
International Journal of… | 1 |
Smart Learning Environments | 1 |
Author
Kinshuk | 8 |
Essalmi, Fathi | 2 |
Ghallabi, Sameh | 2 |
Jemni, Mohamed | 2 |
Lin, Fuhua | 2 |
Yan, Hongxin | 2 |
Ahmed Tlili | 1 |
Bacca-Acosta, Jorge | 1 |
Baldiris, Silvia | 1 |
Chen, Nian-Shing | 1 |
Daniel Burgos | 1 |
More ▼ |
Publication Type
Journal Articles | 8 |
Reports - Research | 4 |
Reports - Descriptive | 2 |
Information Analyses | 1 |
Reports - Evaluative | 1 |
Tests/Questionnaires | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Practitioners | 1 |
Researchers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
Zheng, Lanqin; Kinshuk; Fan, Yunchao; Long, Miaolang – Education and Information Technologies, 2023
Online collaborative learning has been an effective pedagogy in the field of education. However, productive collaborative learning cannot occur spontaneously. Learners often have difficulties in collaborative knowledge building, group performance, coregulated behaviors, learning engagement, and social interaction. To promote productive…
Descriptors: Learning Analytics, Performance, Electronic Learning, Cooperative Learning
Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Smart Learning Environments, 2022
Personalized learning systems use several components in order to create courses adapted to the learners'characteristics. Current emphasis on the reduction of costs of development of new resources has motivated the reuse of the e-learning personalization components in the creation of new components. Several systems have been proposed in the…
Descriptors: Individualized Instruction, Technology Uses in Education, Electronic Learning, Mathematics
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
Yan, Hongxin; Lin, Fuhua; Kinshuk – International Journal of Artificial Intelligence in Education, 2021
Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as…
Descriptors: Learning Analytics, Independent Study, Online Courses, Electronic Learning
Yan, Hongxin; Lin, Fuhua; Kinshuk – Canadian Journal of Learning and Technology, 2022
Self-paced online learning provides great flexibility for learning, yet it brings some inherent learning barriers because of the nature of this educational paradigm. This review paper suggests some corresponding strategies to address these barriers in order to create a more supportive self-paced online learning environment. These strategies…
Descriptors: Electronic Learning, Individualized Instruction, Educational Strategies, Barriers
Ghallabi, Sameh; Essalmi, Fathi; Jemni, Mohamed; Kinshuk – Education and Information Technologies, 2020
With the emergence of technology, the personalization of e-learning systems is enhanced. These systems use a set of parameters for personalizing courses. However, in literature, these parameters are not based on classification and optimization algorithms to implement them in the cloud. Cloud computing is a new model of computing where standard and…
Descriptors: Electronic Learning, Internet, Information Storage, Models
Tortorella, Richard A. W.; Kinshuk; Chen, Nian-Shing – Education and Information Technologies, 2018
Today people learn in many diverse locations and contexts, beyond the confines of classical brick and mortar classrooms. This trend is ever increasing, progressing hand-in-hand with the progress of technology. Context-aware learning systems are systems which adapt to the learner's context, providing tailored learning for a particular learning…
Descriptors: Electronic Learning, Educational Technology, Context Effect, Models