Hexad-12: Developing and Validating a Short Version of the Gamification User Types Hexad Scale
Bibliographic record
Abstract
The Hexad scale is a crucial tool for personalized gamification in user experience (UX) design. However, completing a 24-item questionnaire can increase dropout rates and screen fatigue within online surveys. When included in larger surveys, scale brevity makes a difference. To reduce the time required for the assessment process, we developed and validated a 12-item version of the Hexad scale. To create it, we carried out an exploratory factor analysis on an existing data set to identify appropriate items (n = 882). To validate the 12-item version, we conducted a confirmatory factor analysis on a new data set (n = 1, 101). Our results show that Hexad-12 outperforms the original Hexad scale regarding model fit, reliability, convergent, and discriminant validity. Therefore, Hexad-12 resolves issues found in studies using the original Hexad scale and provides a suitable and swift instrument for concisely assessing Hexad user types in tailored gamification design.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.003 |
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".