Comparing MOOC Adoption Strategies in Europe: Results from the HOME Project Survey
Bibliographic record
Abstract
<p style="margin: 0cm 0cm 12pt 36pt; text-align: justify; line-height: 15pt;"><span style="font-family: 'Georgia','serif'; mso-bidi-font-size: 10.0pt; mso-bidi-font-family: Georgia;" lang="EN-US"><span style="font-size: small;">Much of the literature and the academic discussion about the impact of Massive Open Online Courses (MOOC) in institutional strategic planning has been centred on the US context. However, data shows that although the US are responsible for the largest MOOC platforms and the most successful course provision, it is the European region which accounts for the highest percentage of global MOOC participation. Differently from the US Higher Education system framework, however, in Europe public policy and in particular the European Commission is now driving MOOC institutional uptake.</span></span></p><p style="margin: 0cm 0cm 12pt 36pt; text-align: justify; line-height: 15pt;"><span style="font-family: 'Georgia','serif'; mso-bidi-font-size: 10.0pt; mso-bidi-font-family: Georgia;" lang="EN-US"><span style="font-size: small;">Given the very different institutional, political and cultural contexts, it is interesting to analyse how in these two different regions Higher Education institutions are responding to the challenges of the MOOC phenomena and are integrating it in their own strategic planning.</span></span></p><p style="margin: 0cm 0cm 12pt 36pt; text-align: justify; line-height: 15pt;"><span style="font-family: 'Georgia','serif'; mso-bidi-font-size: 10.0pt; mso-bidi-font-family: Georgia;" lang="EN-US"><span style="font-size: small;">The current research presents the first attempt to conduct a benchmarking study of institutional MOOC strategies in Europe and the US. It's based on a survey launched by the EU-funded project HOME and compares results with a similar survey launched in the US. Results show that are significant differences in how US and European institutions understand the impact of massive forms of open education and also how they perceive the efficiency of digital education and online learning.</span></span></p>
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".