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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".