INDIVIDUALIZATION OF LEARNING IN CANADIAN MULTINATIONAL CORPORATIONS:FOREIGN EXPERIENCE AND WAYS OF IMPLEMENTATION
Bibliographic record
Abstract
The corporations perceive human capital as the greatest potential for competitive advantage, and staff training as a prerequisite for achieving the company’s strategic aim since business success and market competitiveness directly depend on the ability of employees to provide life long learning. In the context of searching the ways of optimizing this process, the aim of our study is to determine the features and opportunities for the development of individualization of learning by means of information and communication technologies in multinational corporations in Canada in order to use their experience in Ukrainian higher education. The set of interrelated general scientific research methods was used to achieve this goal: analysis, synthesis, comparison, generalization, systematization, which were used to study the scientific literature, programs and corporate training courses in multinational corporations in Canada. Analysis of training in Canadian multinational corporations shows that the use of the advanced information and communication technologies in independent non-formal learning, including mobile learning, social networks, Massive Open Online Courses, electronic coaching, corporate blogs, gamification, wikis, etc., contributes to the deepening of individualization of training, and the practice of employee’s independent planning and implementation of his/her learning process is becoming more common. The study concludes that individualization is an important aspect of training in Canadian multinational corporations. The basis of individualization of learning is self-direction, autonomy of students, their willingness to take responsibility for planning and implementation of all stages of their learning.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".