Space Oddity: A Demonstration of the Self-Efficacy Game Elements Framework
Bibliographic record
Abstract
Self-efficacy describes an individual’s perceived capability to accomplish a given task. Self-efficacy theory has long been applied in games for increasing player motivation, performance, and game enjoyment— however, it’s not immediately clear how to design or develop a game that promotes self-efficacy. To explore the effects of self-efficacy in the games research domain, we developed Space Oddity, an arcade shooter game. The primary contribution of this work is a framework for designing self-efficacy supporting game elements in the context of shoot ’em up games that stand to support three of self-efficacy theory’s antecedents (i.e., mastery experiences, vicarious experiences, and social persuasion). Specifically, we identify progress bars, counters, dynamic difficulty adjustment, juicy visuals and sound effects, usernames, tutorials, gameplay teasers, leaderboards, narrative, ghost replays, encouragement from NPCs, verbal praise, and achievement acknowledgements as likely self-efficacy supporting elements. We also offer insights on our preliminary data analysis, but caution against over-interpretation of these results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".