| Title |
Correction to: Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rulebased models
|
|---|---|
| Published in |
International Journal of Educational Technology in Higher Education, October 2022
|
| DOI | 10.1186/s41239-022-00367-1 |
| Authors |
Balqis Albreiki, Tetiana Habuza, Nazar Zaki |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 14% |
| Librarian | 1 | 14% |
| Other | 1 | 14% |
| Lecturer | 1 | 14% |
| Student > Doctoral Student | 1 | 14% |
| Other | 1 | 14% |
| Unknown | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 2 | 29% |
| Computer Science | 2 | 29% |
| Arts and Humanities | 1 | 14% |
| Unknown | 2 | 29% |