Integrating a game design model in a serious video game for learning fractions in mathematics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it