Implementation of professional skills into technical education programs.
Bibliographic record
Abstract
There are limited contemporary Canadian studies regarding the inclusion of professional skills into technical education. Contentions include what skills are requisite and/or prioritized in various industries. This research sought to explore this gap with a range of academic and industry stakeholders. This mixed methods study encompassed questionnaires, document analysis, and interviews/focus groups and included faculty members, students, and industry member representatives. There were 595 who completed the quantitative component and 56 individuals who participated in the qualitative interviews. Questionnaires included learner exist surveys, employer satisfaction surveys, and professional skills ranking instrument. Document analysis of job advertisements supported the development of the instruments. Interviews explored stakeholder nuanced perspectives. Academics, leaders, and industry representatives recognized the importance of integrating professional skills to two-year technical programs, but identified these were not always intentionally taught. While skills were deeply valued, there were barriers to reaching consensus across stakeholder groups about the “set” of skills. Finally, it would require a concerted effort by leaders, teaching academics/instructors, industry representatives, and curriculum designers to select which skills to integrate into the program and support to teach and assess these skills to maximize graduate outcomes. A proposed model – the Model of Professional Skill Development in Technical Education Programs – was created designed to integrate both professional and technical skills within program design and implementation. This model be useful to subject matter experts, curriculum designer, leaders who are keen to ensure integration, teaching and graduate success, and students who want to optimize their success in transitioning from learner to employed graduate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".