A Game Design Plot: Exploring the Educational Potential of History-Based Video Games
Bibliographic record
Abstract
The number of video games that are developed based on real historical events and evidence is increasing. These history-based video games provide learning opportunities to players, but a certain type of such games-first- and third-person shooters-has not been carefully examined for their potentials. Knowing what players say about their game experience-even if the information and knowledge are inaccurate-helps researchers understand what type of learning could happen with such games. In this article, we propose a systematic approach to assessing games as learning environments, using the method of comparing the authenticity of popular history-based video games. Through a qualitative data analysis, we studied players' comments on the web-based communication services, such as game forums, digital distribution platforms, and discussion websites. Casual players' conversations on these websites showed that there exist several learning potentials in the games for players, including building their understanding about history and historical forces of the time, through personally relating to specific events, social artifacts, and places.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".