A Usability Study for Electronic Flight Bag (EFB) Flight Planning Applications on Tablet Devices for Ab-initio Pilots
Bibliographic record
Abstract
The proliferation of mobile technology has prompted the use of tablet devices in the cockpit and during ground operations in general aviation. Due to the increase in affordable and reliable hardware (i.e. iPads etc.), the development of pilot-specific software has led to the creation of a commercial-of-the-shelf (COTS), electronic flight bag (EFB) market. EFBs have many advantages, such as reducing the space requirements for flight documents, enabling faster searching and indexing of files, providing useful tools for flight planning, and providing automatic updates. The increase in availability of mobile technology and flight applications has allowed general aviation enthusiast and ab-initio pilots to utilize EFBs. This paper examines the usability of three of the most popular commercial EFB software programs: Foreflight mobile, Garmin Pilot, FltPlan Go. The usability study was developed for the ab-initio demographic (time), which primarily constitutes pilots who have completed their cross country training. The study assessed 30 ab-initio collegiate flight students on a series of tasks during each flight application. The usability of the applications was based on task success, time to complete the task, efficiency of the application, and learnability. The students also completed a pre survey, the NASA Task Load Index (TLX), System Usability Survey (SUS), and post survey, respectively. An Analysis of Variance (ANOVA) was conducted to compare the scores of the three applications. The results of the study show that Foreflight had the best scores across all metrics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".