Learning by gaming: nonwork-to-work enrichment among successful massive multiplayer online gamers
Bibliographic record
Abstract
Online gaming is stereotypically associated with negative outcomes, partially due to social stigmas. Given the large population of massive multiplayer online (MMO) gamers, in this qualitative study, we explored if and how gaming resulted in positive outcomes by enriching employees’ work. To do so, we interviewed 23 employed adults with extensive gaming experience. Our analysis revealed that MMO gaming resulted in several learning outcomes that were directly related to general workplace skills. We categorised these learning outcomes as affective (i.e. viewing work as solvable puzzles, developing self-confidence, developing self-awareness), behavioural (i.e. leading and working with a team, coaching and developing others, developing social connections, conflict resolution), and cognitive (i.e. gaining knowledge; goal setting, strategising, and planning; adaptability and agility; and problem-solving). Also, we highlighted the social and individual factors that played a role in how learning outcomes were transferred from gaming to work. Our findings broaden the limited scholarship on employee enrichment experiences, extending our understanding of how an individual’s hobby, as an understudied and critical part of the nonwork domain, is associated with the work domain. Our study challenges the common negative stereotypes about gamers and advocates the potential enrichment of workplace skills resulting from gaming during nonwork time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".