Improving mastery of fractions by blending video games into the Math classroom
Bibliographic record
Abstract
Abstract Concepts from the Australian mathematics curriculum on fractions were used as core elements to design three computer games. In each game, the concepts were presented in the form of tangible puzzles, customized to a difficulty level based on student capability. The games were integrated into a single virtual game world, and a fantasy story was used to help build a compelling experience. Five Year 6 classes were used to evaluate the game over four weeks. Three of the classes were provided with the games, and two served as a control. Both the intervention and control groups also covered fractions in class as part of the regular teaching program, consisting of instructor led content combined with access to online resources and activities. Participants completed a diagnostic test before the trial, and again at the end, designed to assess competence in the fractions concepts targeted by the game. Results show that on average students who had access to the game in addition to the regular teaching scored higher than control group students. In particular, looking at just students who started with a lower level of fractions skills, greater improvement was seen in those that had access to the game.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".