Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 0 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Academic Achievement | 1 |
| Academic Persistence | 1 |
| Accuracy | 1 |
| Computer Mediated… | 1 |
| Data Analysis | 1 |
| Discourse Analysis | 1 |
| Discriminant Analysis | 1 |
| Discussion Groups | 1 |
| Dropouts | 1 |
| Information Retrieval | 1 |
| Large Group Instruction | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Author
| Baker, Ryan | 1 |
| Barnes, Tiffany | 1 |
| Bergner, Yoav | 1 |
| Crossley, Scott | 1 |
| McNamara, Danielle S. | 1 |
| Paquette, Luc | 1 |
| Wang, Yuan | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Download full textCrossley, Scott; McNamara, Danielle S.; Baker, Ryan; Wang, Yuan; Paquette, Luc; Barnes, Tiffany; Bergner, Yoav – International Educational Data Mining Society, 2015
Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused…
Descriptors: Online Courses, Large Group Instruction, Information Retrieval, Data Analysis
