Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 2 |
Descriptor
Author
| Christie, S. Thomas | 1 |
| Jarratt, Daniel C. | 1 |
| Jian-Wei Tzeng | 1 |
| Nen-Fu Huang | 1 |
| Olson, Lukas A. | 1 |
| Taijala, Taavi T. | 1 |
| Ting-Wei Huang | 1 |
| Yi-Hsien Chen | 1 |
| Yu-Sheng Su | 1 |
Publication Type
| Reports - Research | 2 |
| Journal Articles | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| High Schools | 1 |
| Higher Education | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Postsecondary Education | 1 |
| Secondary Education | 1 |
Audience
Location
| Taiwan | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkJian-Wei Tzeng; Nen-Fu Huang; Yi-Hsien Chen; Ting-Wei Huang; Yu-Sheng Su – Educational Technology & Society, 2024
Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and…
Descriptors: MOOCs, Artificial Intelligence, Electronic Learning, Student Participation
Download full textChristie, S. Thomas; Jarratt, Daniel C.; Olson, Lukas A.; Taijala, Taavi T. – International Educational Data Mining Society, 2019
Schools across the United States suffer from low on-time graduation rates. Targeted interventions help at-risk students meet graduation requirements in a timely manner, but identifying these students takes time and practice, as warning signs are often context-specific and reflected in a combination of attendance, social, and academic signals…
Descriptors: Dropout Prevention, At Risk Students, Artificial Intelligence, Decision Support Systems
