Introduction and Activation Strategies for Smart Training of Corporate
Bibliographic record
Abstract
Purpose -The purpose of this study is to explore the introduction and activation of smart training for the effective training of vocational ability development of companies in the 4th industrial revolution era, we analyze the present status of smart training introduction and related difficulties and propose concrete activation plan.Research design, data, and methodology -Through the online survey, we tried to confirm the recognition of corporate about smart training.Questionnaires include what are the benefits, expectations, and difficulties of smart training, etc.The survey was conducted from August 21, 2017 to September 4, 2017.A total of 69 companies participated in the questionnaire.The questionnaire results were analyzed through frequency analysis and contents analysis.Based on the results of the questionnaire, we found out the cause of inhibition of smart training activation and suggested activation strategies.Results -The main reason for the provision of smart training is the expectation of the training performance and the recognition that it is possible to provide training in a flexible manner.The effectiveness of smart training operation was evaluated as a high level of contribution to the development of creative training course and the capacity of training institute.As a result of checking factors that hinders the activation of smart training, the most important reason is that the time and cost burden of the training institutes is excessive.The lack of expertise in the design of smart training courses and the burden of employers and trainees.Conclusions -In order to activate smart training, it is necessary to find solutions to the obstacles at the internal or external level of training institutions.The internal barriers to the training organization are lack of internal competence for preparation and course management.In this regard, we need to consider providing consulting, best practices or guidance in the process of designing and operating smart training.On the other hand, as an external obstacle factor, it is necessary to provide incentives to participate in smart training.In addition, further research is needed on strategies that can lead to participation in smart training from the viewpoint of employers and learners.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".