Integrating a game design model in a serious video game for learning fractions in mathematics
Bibliographic record
Abstract
“Serious” video games (SVGs) are increasingly used as supplementary teaching tools for mathematics education. Several studies report their positive impact on student learning. However, these impacts are variable, and the success of the tools cannot be generalized or extended to all settings or disciplines without an in-depth look at the games themselves. Indeed, the impact of the tools depends on several factor, mainly, the quality of the games in terms of educational value and play value. This article summarizes the development of an SVG for learning fractions. It presents the theoretical considerations that guided the game design. The game was also tested in a classroom setting with primary students. An experimental protocol was used to measure the effects of the game on learning. The results of the study demonstrate a positive impact of the game on student learning. Use of the game also led to a significantly greater increase in learning compared to traditional instruction without use of the game. We examine these results and discuss the usefulness and impact of a game design model on SVG effectiveness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".