An Overview of Competency Management for Learning and Performance Support: A Canadian Perspective
Bibliographic record
Abstract
Despite the turbulent economy, recent expenditures on workplace learning in North America have increased. Technology-based methods including tools that enable social learning are making significant gains and account for 39% of all training hours in 2012. A majority of companies are moving from static classroom training to workplace learning that is more interactive and driven by technology. Companies actively experiment with new methods such as personalized learning, performance support, and gamification to encourage employees’ motivation to learn and promote continuous workplace learning, practice and application. However, the divide between the training and competencies people have and the training and competencies companies need still remains. The National Research Council Canada (NRC)’s Learning and Performance Support Systems (LPSS) program, by implementing adaptive and personalization strategies, develops software components for learning, training, performance support and enterprise workforce optimization. These technologies have the potential to facilitate lifelong learning, reduce learning and training costs, and reduce demands on physical infrastructure. Software components being developed for learning, training and performance support also enable streamlined and rapid skill development, as well as reduce time to competency, support informal, personal and personalized learning, increase learner engagement, address workforce optimization and sustainability, and increase operational performance and productivity. An overview of the LPSS system and capabilities is presented along with the results of our review of the current state of competency management in Canada and some challenges in this area, followed by recommendations for further work on competency functionality in the context of the LPSS program.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".