Gamification and Gaming in Cryptocurrency Education: A Survey with Cryptocurrency Investors and Potential Investors
Bibliographic record
Abstract
Introduction In recent years, cryptocurrency has increasingly sparked interest among investors. Many people have invested in this field without adequate knowledge. Existing research has shown that using game design elements can be an effective method of education. Such learning interventions can potentially be a good match for educating market investors, as they provide risk-free simulations for novice investors to gain practical experience without having to be concerned about real financial losses. However, it is unclear how market investors perceive gamified and game-based learning interventions and whether they would adopt them for cryptocurrency education. Research Objectives Our study investigated market investors’ perceptions, needs and expectations regarding the integration of gamification and game-based learning interventions in cryptocurrency education. Methodology We conducted an online survey with n=413 participants, including experienced market investors and people who are interested in cryptocurrency. Within the survey, we presented the mock-ups of two cryptocurrency learning interventions: a gamified cryptocurrency learning application, and a cryptocurrency learning video game. Results From market investors’ perspectives, our study revealed the benefits and drawbacks of incorporating gamification and game design principles to facilitate learning cryptocurrency. We identified the need to develop dynamic, accessible, reliable, and community-building gamified and game-based cryptocurrency learning interventions. Conclusion From our findings, we propose guidance for the integration of gamification and games in cryptocurrency education, and we provide design recommendations for investor-specific cryptocurrency learning interventions.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".