Reshaping Human Factors Education in Times of Big Data: Practitioner Perspectives
Bibliographic record
Abstract
Human Factors Engineering (HFE) is an applied discipline that uses a wide range of methodologies to better the design of systems and devices for human use. Underpinning all human factors design is the maxim to fit the human to the task/machine/system rather than vice versa. While some HFE methods such as task analysis and anthropometrics remain relatively fixed over time, areas such as human-technology interaction are strongly influenced by the fast-evolving technological trend. In times of big data, human factors engineers need to have a good understanding of topics like machine learning, advanced data analytics, and data visualization so that they can design data-driven products that involve big data sets. There is a natural lag between industrial trends and HFE curricula, leading to gaps between what people are taught and what they will need to know. In this paper, we present the results of a survey involving HFE practitioners (N=101) and we demonstrate the need for including data science and machine learning components in HFE curricula.
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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 | 0.058 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".