The Use of e-Learning in Vocational Education and Training (VET): Systematization of Existing Theoretical Approaches
Bibliographic record
Abstract
Vocational education and training (VET) has been facing a lot of challenges lately in the context of geostrategic forces that are shaping our world. Recent technological changes, combined with shifts in global economic power, accelerating urbanization, and demographic changes have put pressure on the VET to become more responsive to the needs of the labour market and society. E-learning has been seen as an effective way of improving the quality of teaching and learning in VET schools due to its various forms. Nevertheless, there has been some disagreement in the litearture on the advantages and disadvantages of the use of of e-learning in VET. Besides, some studies recently reported a decline in enthusiasm about the effects of e-learning in companies. In order to closely examine the effects of e-learning in VET, we conduct a literature review. We then carry out a discussion of the pros and cons with the aim of developing suggestions for the better use of e-learning in VET. The results of the litearture review show that learners and providers of e-learning benefit from it in different ways. In order to minimise the risks involved in using e-learning, a mixture of online and face-to-face events could be used, and adjusted pedagogical concepts should be designed and developed explicitly for e-learning.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".