MOOCs, Graduate Skills Gaps, and Employability: A Qualitative Systematic Review of the Literature
Bibliographic record
Abstract
<p class="3">The increasing costs of higher education (HE), growing numbers of flexible anytime, anywhere learners, and the prevalence of technology as a means to up-skill in a competitive job market, have brought to light a rising concern faced by graduate students and potential graduate employers. Specifically, there is a mismatch of useful skills obtained by students through HE institutions which is evident upon graduation. Faced with this dilemma, “graduate students,” or more specifically newly graduated students, with a with bachelor’s degree, and a growing number of employers are turning to Massive Open Online Courses, or MOOCs, as a complimentary mechanism through which this skills gap may be bridged. </p><p class="3">It is found in the literature that MOOCs are often discussed within the capacity of their development, their retention rates, institutional policies regarding their implementation, and other such related areas. Examinations into their broader uses, benefits, and potential pitfalls have been limited to date. Therefore, this paper aims to analyse the literature highlighting the use of MOOCs as a means to reduce the mismatch in graduate skills. As such, this literature analysis reviews the following relevant areas: higher education and graduate skills gap, today’s graduates and employability, and MOOCs and graduate skills. Through analysing the literature in these areas, this paper identifies gaps in the existing literature. </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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".