Institutional Collaboration on MOOCs in Education—A Literature Review
Bibliographic record
Abstract
<p class="3">This literature review seeks to outline the state of the art regarding collaboration between educational institutions on Massive Open Online Courses (MOOCs) launched in Europe and in the US for the past 10 years. The review explores enablers and barriers that influence national institutional MOOC collaboration, and looks into how existing knowledge about institutional collaboration on e-learning can be used in MOOC collaboration. The review is based on a literature search in databases and on snowballing techniques. It concludes that collaboration on MOOCs can be advantageous in terms of ensuring quality and innovation in the common learning designs, and that—in order to succeed—such projects need strategic and institutional support from all partners involved. Moreover, the review points out barriers concerning the reluctance of individual institutions to engage in national collaboration due to fear of potential loss of their own national branding and the teachers’ hesitancy or passive resistance to new educational platforms and formats.</p>
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How this classification was reachedexpand
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".