Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Context Effect | 2 |
| Learning Analytics | 2 |
| Time Management | 2 |
| Artificial Intelligence | 1 |
| Assignments | 1 |
| At Risk Students | 1 |
| Barriers | 1 |
| Computer Simulation | 1 |
| Data Processing | 1 |
| Data Use | 1 |
| Identification | 1 |
| More ▼ | |
Source
| IEEE Transactions on Learning… | 2 |
Author
| Dougiamas, Martin | 1 |
| Huynh, Du Q. | 1 |
| Khong, Andy W. H. | 1 |
| Liu, Kai | 1 |
| Olive, David Monllao | 1 |
| Reynolds, Mark | 1 |
| Tatinati, Sivanagaraja | 1 |
| Wiese, Damyon | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Liu, Kai; Tatinati, Sivanagaraja; Khong, Andy W. H. – IEEE Transactions on Learning Technologies, 2020
Activity-centric data gather feedback on students' learning to enhance learning effectiveness. The heterogeneity and multigranularity of such data require existing data models to perform complex on-the-fly computation when responding to queries of specific granularity. This, in turn, results in latency. In addition, existing data models are…
Descriptors: Context Effect, Models, Learning Analytics, Data Use
Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses

Peer reviewed
Direct link
