Research on MOOCs in Major Referred Journals
Bibliographic record
Abstract
Over the last decade, several studies have focused on massive open online courses (MOOCs). The synthesis presented here concentrates on these studies and aims to examine the place held by content in these studies, especially those produced between 2012 and 2018: sixty-five peer reviewed papers are identified through five major educational technology research journals. The analysis revealed that these research articles covered a wide diversity of content. Content was mainly defined in terms of objectives of MOOCs, prerequisites required for participation in the MOOC, types of learning scenarios, and, though rarely, through the strategies used to convey content. In addition, empirical studies adopted a variety of conceptual frameworks which focused mainly on learning strategies without relating to the content in question. Finally, content was seldom considered as a research object. These results can provide MOOC researchers and instructors with insights for the study and design of MOOCs by taking into account the specificity of their content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.029 | 0.044 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".