Assessing Blue-Collar Workers Cognitive Skills Through Gamified Tasks and Analytics
Bibliographic record
Abstract
This study presents a gamified framework for assessing the cognitive abilities of blue-collar workers in the manufacturing sector. Conventional cognitive testing methods, such as the Montreal Cognitive Assessment (MoCA), fail to capture the dynamic and high-pressure decision-making skills required for operating complex machinery. To address this gap, six Python-based mini-games were developed using Pygame to evaluate attention, memory, spatial reasoning, problem solving, and decision-making. A cohort of 20 industrial workers was assessed, and the total gamified scores demonstrated a strong positive correlation with MoCA results (r = 0.74, R2= 0.682). Furthermore, cognitive retraining and job-role reassignment of a welding operator showed significant improvements in both cognitive performance and operational efficiency. These findings establish the novelty and significance of gamified cognitive assessment as a scalable, engaging, and cost-effective approach to workforce optimization. The research outcomes contribute to industrial safety, productivity enhancement, and informed workforce deployment strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".