A strategic response to MOOCs: How one European university is approaching the challenge
Bibliographic record
Abstract
<p class="normal">This paper briefly outlines some of the macro level claims, counter-claims and unresolved debates surrounding the rapid growth of Massive Open Online Courses (MOOCs) in Higher Education. It then reports insights, experiences and perceptions of those charged with developing a strategic institutional response to the challenges and opportunities presented by the MOOC movement framed within a wider European context. A description of the key drivers, strategic deliberations and major decision points at Dublin City University (DCU) is provided along with brief analysis of the advantages and disadvantages of a range of MOOC options set against an increasingly complex and rapidly evolving technology-enhanced learning terrain. In reflecting on this micro level experience, informed by lessons from the burgeoning literature on MOOCs, the paper aims to demonstrate the value of aligning key decisions with well-defined institutional drivers, which are used to help compare and contrast the affordances of different MOOC platforms. Finally, a number of strategic questions are presented that may help guide future decisions about the adoption of MOOCs by other institutions. </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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.033 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".