Factors Affecting Satisfaction with Serious Games - Direct, Mediated and Higher-Order Constructs
Bibliographic record
Abstract
Serious games are software combining the serious dimension of learning with the playful dimension of games. They are being increasingly used to complement and enhance more traditional teaching practices by virtually recreating situations that occur in reality, enabling learners to develop procedural knowledge and honing skills. Little research exists identifying factors that affect learners' satisfaction with serious games. We sampled $\mathrm{n}=110$ students enrolled in university classes employing serious games. Analysis was conducted using SmartPLS. Results show that learners' satisfaction with serious games is influenced directly by entertainment, effectiveness, sense of control and fidelity. An alternative theoretically justifiable model was considered with second-order factors and mediation. It showed good results based on validation metrics. We conclude that whereas some factors identified in the literature impact satisfaction directly, others do so through mediation and as elements of higher order concepts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".