Assessing Ethoshunt as a Gamification-Based Mobile App in Ethics Education: Pilot Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Gamification has remarkable potential in the learning space. The process of creating a gamified system and its influence on human behavior reflect the interaction between educators and machines. OBJECTIVE: The purpose of this pilot study was to present Ethoshunt as a gamification-based mobile app that can be used in teaching and learning ethics. METHODS: This study involved a mixed-methods research design. The researchers surveyed 39 undergraduate students who were introduced to Ethoshunt in order to examine the relationships between mobile app usability and positive emotions, ethical competency, and user experience. Affinity diagramming was used as a tool to organize the opinions and experiences of participants using featured gamification elements. RESULTS: Game dynamics and game mechanics explained the functionality of Ethoshunt. In addition, the learning flow through Ethoshunt was discussed. Overall, the findings were positive, and mobile app usability had the strongest relationship with positive emotions (r=0.744, P<.001), followed by ethical competency (r=0.686, P<.001) and user experience (r=0.614, P<.001). CONCLUSIONS: Positive emotions could be perceived as an important dimension in the development and usability of Ethoshunt. The researchers suggest that the gamification-based mobile app advocated in this study may provide ideas for ethics educators who wish to develop a technology-mediated learning environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".