Information technologies in corporate training: trends and approaches
Bibliographic record
Abstract
Problem and goal. Within the framework of the study, based on the data of the Workplace Learning Report study, specialists from the USA, Canada and other countries, the transformation of corporate training over the past decades was analyzed, the main problems and challenges of companies/enterprises in the process of additional professional training of employees and ways to solve them were identified. The main problems of corporate training at the present time, as in the past, include budget deficit and search for free intervals in the schedules of employees for educational sessions. And the solution was the growth of online training, the use of online platforms, which made it easier to find time in the sche- dule of employees for training, create opportunities for flexible editing of educational content, and for managers it was easier to evaluate additional professional training thanks to the control tools built into online platforms. Methodology. However, it turned out that not all age categories of employees are ready to expand online training: older age workers prefer traditional or mixed training, as opposed to young people. Results. The study found that the degree of digitalization correlates with the size of the company: the comparative effectiveness of digital tools for additional professional education increases with the scale of the system in which they are applied: a deployed digital educational platform requires very few resources to expand to new branches and employees, rather than classical educational formats that require personal participation of teaching staff. Conclusion. The main trends in the development of corporate training in the coming years are described.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".