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
| At Risk Students | 1 |
| College Students | 1 |
| Educational Discrimination | 1 |
| Foreign Countries | 1 |
| Learning Analytics | 1 |
| Models | 1 |
| Potential Dropouts | 1 |
| Prediction | 1 |
| Privacy | 1 |
| Student Records | 1 |
Author
| Deho, Oscar Blessed | 1 |
| Joksimovic, Srecko | 1 |
| Li, Jiuyong | 1 |
| Liu, Jixue | 1 |
| Liu, Lin | 1 |
| Zhan, Chen | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Australia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkDeho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
