Building Professional Skills and Reducing Student Stress with Competency-based Education: An Exploratory Case Study
Bibliographic record
Abstract
Competency-Based Education (CBE) is an alternative pedagogical model that emphasizes mastery of skills and professional readiness over traditional metrics such as ‘seat time’ or grades. This exploratory case study investigated students' perceptions of implementing a CBE format in a second-semester Wellness Coaching Program (WCP) course at Humber Polytechnic. Using an anonymous survey of nine students, a thematic analysis was conducted on the open-ended responses. The results revealed five principal themes: (1) balancing flexibility with structure, (2) enhancing practical skills for career preparedness, (3) supportive learning through instructor and peer engagement, (4) navigating challenges in course design and workload, and (5) positive impact of mastery-based assessment. Overall, students viewed the CBE format favourably, noting decreased stress and increased confidence in professional skills. However, some difficulties were reported regarding the absence of firm deadlines and the volume of coursework. The findings suggest that CBE can offer flexible, relevant, and practical learning experiences that enhance access and equity for post-secondary learners. Educators and institutions, especially polytechnics, should consider these insights when designing CBE formats to prepare students for the future of work.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".