Optimizing a workplace learning pattern: a case study from aviation
Bibliographic record
Abstract
Purpose – This study aims to contribute to current research on team learning patterns. It specifically addresses some negative perceptions of the job performance learning pattern. Design/methodology/approach – Over a period of three years, qualitative and quantitative data were gathered on pilot learning in the workplace. The instructional modes included face-to-face classroom-based training; pilots assessing pre-recorded videos in classroom-based training; pilots assessing videos with fellow pilot of similar rank (paired training); pilots undertaking traditional 4-hour simulator session with 1-hour debriefing using a variety of technologies for replaying the simulator session; and pilots undertaking 2-hour simulator sessions with extended 3-hour debriefing utilizing simulator replay video. Findings – Although traditional classroom-based, face-to-face instruction was viewed as acceptable, pilots who critically assessed the practice of other pilots in pre-recorded videos felt empowered by transferring classroom instruction to the workplace. The study also establishes a need to determine the correct balance between high-workload simulator training and low-workload debriefing. Research limitations/implications – A move towards developing a typology for workplace learning patterns was viewed negatively if job performance was the focus. However, pilot practitioners felt empowered when provided with the right mix of performance-oriented learning opportunities, especially when these provided an appropriate mix of high-fidelity simulations with time for reflection on practice. Practical implications – By focusing on one learning pattern – job performance – the paper demonstrates the benefits of learning via a variety of instructional modes. Whereas aviation has a unique workplace environment, many other high- and low-risk industries are acknowledging the impact of technical and non-technical skills on job performance. This may suggest that findings from this study are transferable across a broader range of workplace settings. Originality/value – The findings demonstrate that broadening research across many professional workplace settings may assist in developing a more robust framework for the micro-organization of each workplace learning pattern.
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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".