Trends and Patterns in Massive Open Online Courses: Review and Content Analysis of Research on MOOCs (2008-2015)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="3">To fully understand the phenomenon of massive open online courses (MOOCs), it is important to identify and map trends and patterns in research on MOOCs. This study does so by reviewing 362 empirical articles published in peer-reviewed journals from 2008 to 2015. For the purpose of this study, content analysis and discourse analysis were employed to analyze the articles. Accordingly, the trend line showing the number of articles per year indicates that the extent of research on MOOCs is likely to increase in the coming years. In terms of research areas, the findings reveal an imbalance and three research areas out of fifteen constitute more than half of all research on MOOCs. With regard to types of MOOCs, related literature is dominated by research on xMOOCs. The discourse in MOOC articles takes a mostly neutral standpoint, articles with a positive outlook outweigh those that are negative, and there is an increase in a more critical discourse. Theoretical or conceptual studies are preferred by researchers, although MOOC research generally does not benefit from being viewed through theoretical or conceptual lenses.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it