Exploring Media Literacy and Computational Thinking: A Game Maker Curriculum Study.
Bibliographic record
Abstract
Abstract: While advances in game‑based learning are already transforming educative practices globally, with tech giants like Microsoft, Apple and Google taking notice and investing in educational game initiatives, there is a concurrent and critically important development that focuses on game construction pedagogy as a vehicle for enhancing computational literacy in middle and high school students. Essentially, game construction‑based curriculum takes the central question do children learn from playing games to the next stage by asking (what) can children learn from constructing games? Founded on Seymour Papert s constructionist learning model, and developed over nearly two decades, there is compelling evidence that game construction can increase student confidence and build their capacity towards ongoing computing science involvement and other STEM subjects. Our study adds to the growing body of literature on school‑based game construction through comprehensive empirical methodology and evidence‑based guidelines for curriculum design. There is still debate as to the utility of different software tools for game construction, models of scaffolding knowledge, and evaluation of learning outcomes and knowledge transfer. In this paper, we present a study we conducted in a classroom environment with three groups of grade 6 students (60+ students) using Game Maker to construct their own games. Based on a quantitative analysis and a qualitative discussion we organize results around several core themes that speak to the field of inquiry: levels of computational literacy based on pre‑ and post‑tests; gender‑based attitutdes to computing science and programming based on a pre‑ and post‑survey; and the relationship between existing media literacy and performance in programming as part of the game construction curriculum. Significant results include some gender differences in attitudes towards computers and programming with boys demonstrating slightly higher confidence and performance. We discuss the complex reasons potentially contributing
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".