Bibliographic record
Abstract
Purpose Very little effort has been dedicated to the teaching of serious game design and development. At the post-secondary level, very few courses dedicated to serious game design and development exist. At the K-12 level, although (entertainment) game design and programming instruction are becoming more widespread, serious game design and development is ignored. This study aims to present a series of lesson plans that allow K-12 teachers to introduce students to serious game design and development. Design/methodology/approach The lesson plans include both a didactic and applied component and are intended to provide students with an introduction to serious games and their design and development while making students aware of the many career paths within this exciting growing domain. They can also be completed entirely remotely lending themselves nicely to online instruction to facilitate the COVID-19 shutdowns and the resulting move to e-learning. Findings Although several high-school teachers and several elementary school children were consulted during the development of the lesson plans, the lesson plans have only recently been made available, and therefore, there is a lack of any teacher or student feedback available regarding their use. Informally, several elementary school children found the lessons to be fun, interesting and informative. Originality/value There are currently no existing courses or lesson plans focusing on serious game design and development at the K-12 level, thus making this set of lesson plan novel and unique.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".