Aspects of Competence-Based Education as Footholds to Improve the Connectivity Between Learning in School and in the Workplace
Bibliographic record
Abstract
Recent developments in competence-based education have motivated institutions of vocational education and training (VET) to improve the links or connectivity between learning in school and learning in the workplace, which has been a problem for decades. In previous research, a theoretical framework describing the underlying aspects of competence-based education was developed. In this study, three aspects of this framework were used to analyse connectivity between learning in school and learning in the workplace. These aspects were: i) authenticity, ii) selfresponsibility, and iii) the role of the teacher as expert and coach. Three stakeholder groups (i.e., students, teachers, and workplace training supervisors) involved in secondary VET programs in the field of life sciences in the Netherlands were questioned on these aspects. Based on their interviews, it is concluded that these aspects provide information about the process of connectivity. Because stakeholder groups hold different conceptions of workplace learning and often do not communicate adequately about mutual responsibilities, the implementation of these aspects of competence-based education has not significantly improved the connectivity situation. Nevertheless, these aspects of competence-based education can guide stakeholder groups in making clearer agreements about mutual responsibilities, which may improve connectivity in the future.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".