The employer potential of MOOCs: A mixed-methods study of human resource professionals’ thinking on MOOCs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
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.
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 | 0.019 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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 it