The Military Learner: The Acceptance of New Training Technology for C-130 Aircrews
Bibliographic record
Abstract
The problem in this qualitative embedded single-case study was that business and military organizations have shrinking budgets, which has caused conflicting priorities for training funds. This has forced training managers to develop alternative instructional programs to reduce costs, which sometimes means replacing people with technology. To be useful, the new technology must be accepted and used by learners. During military training, certain programs require learners to use a new technology despite the possible lack of acceptance of that technology. Researchers do not know how military learners accept new technology that is mandatory to use. The purpose of this case study was to understand how military learners use new technology by exploring the experiences of learners who have used a 360-degree training program. A purposeful sample of 18 participants who attend C-130 flight training was selected from a military base in the Southern United States. The study included structured and open-ended questions to explore learner experiences. The objectives were to describe how the learners accept new technology and describe how learners perceive the value of the training. The findings were that the participants have different learning styles, they take acceptance cues from their instructors, and they need technology to be easy to use. To reach a conclusion, this study applied the third version of the Technology Acceptance Model to a mandatory learning situation in a military context. The recommendations were that new technology should be developed for easy access; that instructors should employ all the support facilities necessary to use new technology; and that new technology should be easy to use to gain participant acceptance. Future recommendations are to expand qualitative studies of technology acceptance in mandatory training situations for business and industry.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".