Micro-Credentials in TVET: An Analysis of Trainers’ Perceptions, Challenges, and Benefits in Bridging Skill Gaps in Tertiary Education
Bibliographic record
Abstract
This paper focuses on the phenomenon of micro-credentials and attempts to analyze the trainers’ perceptions, challenges, and benefits in bridging skill gaps in tertiary education among the offering TVET institutions in Kenya. The target population was 108 TVET senior lecturers selected from TVET institutions in both Makueni and Machakos Counties. A standardized questionnaire was used to collect data from 36 respondents; a pilot study was conducted among 10 respondents to guarantee both validity and reliability of the research instruments. The study concluded that TVET institutions were ready for the introduction of Micro Credentials in their institutions. The conclusion was based on the fact that 94% of the lecturers supported the introduction of the Micro Credentials in TVET institutions, and they considered micro-credentials as complementary to the tertiary education. Moreover, 61.1% saw Micro Credentials as a fundamental for bridging skill gaps within the traditional tertiary education. Moreover, Micro Credentials effectively reduce inequality by enhancing more access to training due to their low cost and flexibility. Finally, the hindrances in starting Micro Credentials in the TVET institutions are solvable; for example, lack of quality and standardization framework. Therefore, what TVET institutions need to do is to set a body responsible for quality assurance standards regarding the credibility of Micro Credentials qualifications. The study further recommended that: time was ripe to introduce Micro Credentials in the TVET institutions. Finally, the study found that there was a high possibility of succeeding in introducing micro-credentials in TVET institutions because the staff overwhelmingly supported the idea.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".