MOOCs, Graduate Skills Gaps, and Employability: A Qualitative Systematic Review of the Literature
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 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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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 | 0.013 | 0.016 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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