The evaluation of learning transfer of industry skills council (ISC) training programs using success case method
Bibliographic record
Abstract
Purpose Industry skills council (ISC) in Korea is at an earlier stage in terms of its formation and incubation. As a governance model similar to sector councils in Canada and UK, it still requires training and development of talents who work for ISCs. The purpose of this study is to analyze the effectiveness of training programs that are currently provided to personnel of the ISC to foster their learning systematically and to develop measures for effectiveness of the training programs. Design/methodology/approach This study evaluated the training program for the staff of the ISC secretariat as a tool to activate the councils’ main functions. In terms of methodology, we developed an effective training model to measure the training transfer and used it as an analytical framework for evaluation. Success case method was applied to identify the best case of training transfer that reinforces the role and function of ISC. Findings Learning transfer can help not only the transfer of the learning contents but also the role of the organization that the members belong to and strengthen the function of the ISC. By transferring the content matter of the learning, it can help strengthen the capacity of members to carry out the roles and functions of the ISC, and further strengthen the functions of the council and the role of key players in labor markets. Research limitations/implications An effective training model for the personnel of national sectoral bodies or non-profit organization can be further investigated. Practical implications The learning transfer evaluation model for ISC staff has unique characteristics that are different from previous studies. ISC has the characteristics of public goods that are established with government support and are active in developing human resources in each industry sector. Originality/value Incubating ISC in South Korea is at an earlier stage in terms of research and policy practice. The research findings in this study lay the foundations for further empirical explorations.
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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 | 0.048 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".