Gamification and Soft Skills Assessment in the Development of a Serious Game: Design and Feasibility Pilot Study
Bibliographic record
Abstract
BACKGROUND: The advent of new technologies has had a profound impact on the labor market, transforming the way we work and interact with each other. With the rise of digital tools and platforms, gamification has emerged as a powerful technique for enhancing productivity and engagement in various fields, including human resource management. In particular, gamification has been found to be effective in developing and assessing soft skills, which play a critical role in determining the success of individuals, teams, and organizations. OBJECTIVE: We present a serious game that identifies the most sought-after skills in the job market and offers feedback, and we provide a set of guidelines for the creation of serious games. METHODS: We present the serious game Among the Office Criticality (AOC). The AOC game structure involves a set of sequence analysis techniques, which is known as process mining. RESULTS: The pilot study findings indicate that the game is both engaging and beneficial to subjects, suggesting that the results align with current theoretical perspectives. Furthermore, the study suggests that the obtained data can be extended to the broader population. CONCLUSIONS: This study illustrates a serious game structured according to the needs of the labor market and developed to put the user at the center, using evaluation techniques consistent with the literature, with the aim of constituting an interdisciplinary approach suitable for adequately assessing users and creating value for them.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".