It’s Going to be Amazing: Exploring Children’s Game Play and Making
Bibliographic record
Abstract
This paper reports on the results of a game-based learning study that had a twofold purpose: first we sought to find out what our study participants (ages 10-13) could tell us about their video game play habits at home and second to explore students use ofSuper Mario Maker 2 software on the Nintendo Switch Lite to design their own games. The study took place in two schools, one middle school and one elementary school, in the suburbs of a medium-sized city on Canada’s West coast. Two grade 6 classes participated in the study (n=44), and played Super Mario Maker 2 over 9 days, in pairs. Overall, the study found that all students had prior gameplay experience, meaning that no students struggled with the controls, nor did they need much assistance to build their own games, even though only a handful of participants reported having prior coding and/or game design experience. We also found that gameplay was a common leisure activity for most participants, with girls demonstrating significant skill and familiarity with games, challenging previous gender disparities in game play. This short intervention demonstrates how a commercial off the shelf game can be used to support students’ design-based thinking and making, with all participants managing to design a playable level by the end of the study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".