Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Accountability | 1 |
| At Risk Students | 1 |
| Dropouts | 1 |
| Information Retrieval | 1 |
| Learning Analytics | 1 |
| Models | 1 |
| Online Courses | 1 |
| Prediction | 1 |
| Predictor Variables | 1 |
| STEM Education | 1 |
| Social Sciences | 1 |
| More ▼ | |
Source
| Journal of Educational… | 1 |
Author
| Pei, Bo | 1 |
| Xing, Wanli | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkPei, Bo; Xing, Wanli – Journal of Educational Computing Research, 2022
This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…
Descriptors: At Risk Students, Learning Analytics, Information Retrieval, Models
