Impact of Gamified eLearning on Learning Outcomes for Municipal Employees Aged 50 and Over in Ontario, Canada: An Experimental Study
Bibliographic record
Abstract
The purpose of this quantitative, experimental, causal-comparative, posttest-only, control-group study is to determine if there is a difference in system enjoyment, effort expectancy, and performance expectancy for male and female employees aged 50 years or older at a corporate municipality in Ontario, Canada who participated in eLearning programs with and without game elements. The sample consisted of 38 male and 64 female employees aged 50 years or older who participated in eLearning and then completed an Extended Unified Theory of Acceptance and Use of Technology questionnaire. Data from the questionnaires was analyzed using a two-way multivariate analysis of variance. The results of the study indicated that the interaction effect between gender and eLearning type (gamified vs. non-gamified) on system enjoyment, effort expectancy, and performance expectancy was not statistically significant; the main effect of eLearning type (gamified vs. non-gamified) on system enjoyment, effort expectancy, and performance expectancy was statistically significant; and the main effect of gender on system enjoyment, effort expectancy, and performance expectancy was not statistically significant. The conclusions of the study are that gamification strategies can be applied broadly across male and female municipal employees aged 50 or older without significant variation in impact. Game elements in eLearning programs may influence user perceptions of system enjoyment, effort expectancy, and performance expectancy, but this effect does not vary significantly by gender. Future research should include using a larger sample, extending the duration of the study, and designing a mixed study with both quantitative and qualitative instruments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".