Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Academic Achievement | 2 |
| Algorithms | 2 |
| Comparative Analysis | 2 |
| Learner Engagement | 2 |
| Learning Analytics | 2 |
| Access to Education | 1 |
| Barriers | 1 |
| Behavior Patterns | 1 |
| Business Schools | 1 |
| College Attendance | 1 |
| Data Analysis | 1 |
| More ▼ | |
Author
| David Lefevre | 1 |
| Fikes, Tom | 1 |
| Mauricio Barahona | 1 |
| Meaney, Michael J. | 1 |
| Robert L. Peach | 1 |
| Sophia N. Yaliraki | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
| Higher Education | 2 |
| Postsecondary Education | 2 |
Audience
Location
| United Kingdom (London) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textMeaney, Michael J.; Fikes, Tom – Journal of Learning Analytics, 2023
This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a…
Descriptors: MOOCs, Ethics, Equal Education, Socioeconomic Status
Direct linkRobert L. Peach; Sophia N. Yaliraki; David Lefevre; Mauricio Barahona – npj Science of Learning, 2019
The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal…
Descriptors: Learning Analytics, Web Based Instruction, Online Courses, Learner Engagement
