Playing and Learning: An iPad Game Development & Implementation Case Study | Jouer et apprendre : une étude de cas du développement et de la mise en œuvre d’un jeu sur iPad
Bibliographic record
Abstract
There is a great deal of enthusiasm for the use of games in formal educational contexts; however, there is a notable and problematic lack of studies that make use of replicable study designs to empirically link games to learning (Young, et al., 2012). This paper documents the iterative design and development of an educationally focused game, Compareware in Flash and for the iPad. We also report on a corresponding pilot study of 146 Grades 1 and 2 students playing the game, a paper and pencil related activity and completing a pre- and post-test. The paper outlines preliminary findings from the play testing, which included high levels of student engagement, an approaching statistical improvement from pre- to post-test, and a discussion of the improvements that needed to be made to the game following the pilot study. L’utilisation du jeu dans les contextes éducatifs officiels suscite beaucoup d’enthousiasme. Cependant, le manque d’études qui utilisent des modèles pouvant être répétés pour relier les jeux et l’apprentissage de manière empirique est remarquable et problématique (Young et coll., 2012). Cet article documente la conception et le développement itératifs d’un jeu aux accents éducatifs, Compareware, en Flash et pour l’iPad. Nous traitons également d’une étude pilote correspondante dans le cadre de laquelle 146 élèves de 1re et 2e année ont joué au jeu, réalisé une activité connexe à l’aide de crayons et de papier et passé des tests avant et après. L’article résume les conclusions préliminaires des essais du jeu, y compris des taux élevés d’engagement des élèves, l’amélioration statistique entre les tests avant et après le jeu, ainsi qu’une discussion des améliorations à faire au jeu après l’étude pilote.
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.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".