Points and the Delivery of Gameful Experiences in a Gamified Environment: Framework Development and Case Analysis
Bibliographic record
Abstract
BACKGROUND: Points represent one of the most widely used game mechanics in gamification. They have been used as a means to provide feedback to users. They visually show user performance and are used along with other game mechanics to produce synergy effects. However, using points without analyzing the application environment and targets adversely affects users. OBJECTIVE: This study aims to identify the problems that users encounter when points are applied improperly, to solve problems based on an analysis of previous studies and actual point use cases, and to develop a point design framework to deliver gameful experiences. METHODS: Three problems were identified by analyzing previous studies. The first problem is points that only accumulate. The second is points that emphasize a user's difference from other people. The third pertains to the reward distribution problem that occurs when points are used as rewards. RESULTS: We developed a framework by deriving 3 criteria for applying points. The first criterion is based on the passive acquisition approach and the active use approach. The second criterion is used to classify points as "high/low" and "many/few" types. The third criterion is the classification of personal reward points and group reward points based on segmentation of the reward criteria. We developed 8 types of points based on the derived point design framework. CONCLUSIONS: We expect that some of the problems that users experience when using points can be solved. Furthermore, we expect that some of the problems that arise when points are used as rewards, such as pointsification and the overjustification effect, can be solved. By solving such problems, we suggest a direction that enables a gameful experience for point users and improves the core value delivery through gameful experiences. We also suggest a gameful experience delivery method in the context of the ongoing COVID-19 pandemic.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".