Resource requirements and costs of developing and delivering MOOCs
Bibliographic record
Abstract
<p>Given the ongoing alarm regarding uncontrollable costs of higher education, it would be reasonable to expect not only concern about the impact of MOOCs on educational outcomes, but also systematic efforts to document the resources expended on their development and delivery. However, there is little publicly available information on MOOC costs that is based on rigorous analysis. In this article, we first address what institutional resources are required for the development and delivery of MOOCs, based on interviews conducted with 83 administrators, faculty members, researchers, and other actors in the MOOCspace. Subsequently, we use the ingredients method to present cost analyses of MOOC production and delivery at four institutions. We find costs ranging from $38,980 to $325,330 per MOOC, and costs per completer of $74-$272, substantially lower than costs per completer of regular online courses, by merit of scalability. Based on this metric, MOOCs appear more cost-effective than online courses, but we recommend judging MOOCs by impact on learning and caution that they may only be cost-effective for the most self-motivated learners. By demonstrating the methods of cost analysis as applied to MOOCs, we hope that future assessments of the value of MOOCs will combine both cost information and effectiveness data to yield cost-effectiveness ratios that can be compared with the cost-effectiveness of alternative modes of education delivery. Such information will help decision-makers in higher education make rational decisions regarding the most productive use of limited educational resources, to the benefit of both learners and taxpayers.</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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".