A Novel Application of Gamification for Collecting High-Level Design Information
Bibliographic record
Abstract
This paper presents a novel application of gamification for collecting high-level design descriptions of objects. High-level design descriptions entail not only superficial characteristics of an object, but also function, behavior, and requirement information of the object. Such information is difficult to obtain with traditional data mining techniques. For acquisition of high-level design information, we investigated a multiplayer game, “Who is the Pretender?” in an offline context. Through a user study, we demonstrate that the game offers a more fun, enjoyable, and engaging experience for providing descriptions of objects than simply asking people to list them. We also show that the game elicits more high-level, problem-oriented requirement descriptions and less low-level, solution-oriented structure descriptions due to the unique game mechanics that encourage players to describe objects at an abstract level. Finally, we present how crowdsourcing can be used to generate game content that facilitates the gameplay. Our work contributes towards acquiring high-level design knowledge that is essential for developing knowledge-based CAD systems.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".