The employer potential of MOOCs: A mixed-methods study of human resource professionals’ thinking on MOOCs
Bibliographic record
Abstract
While press coverage of MOOCs (massive open online courses) has been considerable and major MOOC providers are beginning to realize that employers may be a market for their courses, research on employers’ receptivity to using MOOCs is scarce. To help fill this gap, the Finding and Developing Talent study surveyed 103 employers and interviewed a subset of 20 about their awareness of MOOCs and their receptivity to using MOOCs in recruiting, hiring, and professional development. Results showed that though awareness of MOOCs was relatively low (31% of the surveyed employers had heard of MOOCs), once they understood what they were, the employers perceived MOOCs positively in hiring decisions, viewing them mainly as indicating employees’ personal attributes like motivation and a desire to learn. A majority of employers (59%) were also receptive to using MOOCs for recruiting purposes—especially for staff with technical skills in high demand. Yet an even higher percentage (83%) were using, considering using, or could see their organization using MOOCs for professional development. Interviews with employers suggested that obtaining evidence about the quality of MOOCs, including the long-term learning and work performance gains that employees accrue from taking them, would increase employers’ use of MOOCs not just in professional development but also in recruiting and hiring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".