Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 1 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Academic Persistence | 1 |
| Artificial Intelligence | 1 |
| At Risk Students | 1 |
| Dropouts | 1 |
| Elementary Secondary Education | 1 |
| High School Students | 1 |
| Influences | 1 |
| Large Group Instruction | 1 |
| Learning Modalities | 1 |
| Middle School Students | 1 |
| Models | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Author
| Ding, Wenbiao | 1 |
| Li, Hang | 1 |
| Liu, Zitao | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Elementary Secondary Education | 1 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textLi, Hang; Ding, Wenbiao; Liu, Zitao – International Educational Data Mining Society, 2020
With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which…
Descriptors: At Risk Students, Online Courses, Elementary Secondary Education, Learning Modalities
